Live improvisation manipulated using MaxMSP Jitter. Each circle represents a microphone; one close to the percussion (left), one near the piano (center), and one attached to the bass (right). These microphones recorded improvisation  for a period of time related to the 4:5:6 ratios of a major triad (in this case 30, 24, and 20 seconds). Initial recording is reflected in the circles’ first rotation. The program then plays back this 120 second periodic cycle, developing each idea by triggering playback for random short durations based on the ratios found between I IV and V triads. The period is further developed during another 120 second period with longer playback durations. The circles rotate when their sound is being played. After two full periods the captured improvisation is considered “developed” and all three are played back in full.

Live manipulation of sound via a USB footpedal changes panning (which is sometimes latched to the playback durations), volume, and pitch/speed. Trio Cycles is an experiment in discovering new periodic frameworks for improvisation. In jazz song form there is often a 12, 16, 24, or 32 measure periodic form. Many times these forms have a V-I or I-V-I harmonic scheme; sometimes they use a bridge set in a contrasting key, nondiatonic chord sequences, novel cadential schemes, etc. Can the durable and beautiful rational relationships of the tonal system can be extended into the phrase domain–can the mathematics of harmony can be used to create a mathematics for formal improvisation? These Trio Cycles have a periodic form based on this idea.

The center example uses three click tracks to solidify these relationships.

 

I feel like Don Music from Sesame Street. He would try to remember simple songs and compose new ones, but had trouble finishing his ideas. Finally he’d smash his head on the piano keys in abject frustration, bust of Beethoven serenely looking on. Yesterday, Don Music-esque, I had to force myself to walk away from a MaxMSP patch I’d been working on the better part of five days and admit I’d been defeated, or at least temporarily bested, by the “simple” interactive trio pieces I thought would be easy to program.

My overarching issue has been a ridiculous sense that between the day that Joon Lee was good enough to give me two dates at his great club Blue Whale (Feb 25) and the night of the first gig (April 22) I’d be able to formulate, program, rehearse, and feel good about two hours’ worth of all new music. In addition I thought that this new music would be neither jazz-y charts of one or two pages nor through composed pieces but instead would put into ensemble format some heady ideas about new frameworks for improvisation, the computer as improvisational foil, and extended rhythm as song form. Clearly I was way too ambitious; trying to squeeze a good five years of work into a scant two months.

By the time I figured out what I wanted to do and had written a couple bits and pieces the first rehearsal was nigh. This rehearsal was very fun and low key; following a thrilling impromptu performance with Bevan Manson March 27 I definitely feel good about this ensemble’s ability to play freely but within a shared sense of formal, harmonic, and rhythmic space. Indeed this is a great trio; I’d feel pretty good just taking the stage, no music or prep, and going for it. Brad Dutz’ flexibility, timbral range within percussion, and solid real-time decision making work great with Bevan’s wide open approach to structured improvisation–I’m lucky to have such a great band!

As soon as this rehearsal was done I dove into MaxMSP programming. I had sketches for some pieces and got going well; figured out how to record three channels into, name then uniquely, access them remotely. Then mayhem set in–I started to get off-script ideas about how to scale time for my first piece and spent the better part of two days struggling with how to execute some procedural math. Reality loomed: no way was I going to have three or so functional interactive pieces ready to rehearse and fine tune. Don Music time! Yesterday I had to take a break, and today too. My composer/formalist mind got fried and ideas were collapsing fast. It’s nice to compose solo compositions and write windy papers filled with obtuse concepts; the application of this stuff to ensemble composing is a time-intensive process and the true test of my ideas.

In my last post I wrote that “to evolve as musicians I’m convinced we must also evolve new ways of envisioning, rehearsing, and discussing music.” This is surely true, and I want to evolve musically in these concerts. My sense of stuck-ness within the world of music requires that next steps feel innovative. The rub is that innovation takes time, involves risk, and invites failure. I hoped unrealistically to prepare new music that would feel finished and risk-free by April 22. Earlier composing/performing work with the Crooks Band and New Power Trio were challenging and felt new-ish, but were also inside familiar envelopes–chart-based jazz and aurally mediated trio composition–that felt less risky. In the last few years my blocks to new work that felt right and progressive have been overwhelming–specters of unreceptive musicians and lousy outcomes led me back to school to seek out new ideas rather than risk failure. Now I need to put some ideas into action, and it hurts!

One heartening thought goes something like this; 150 years ago there was no well formulated American tradition for improvising over song form. 100 years ago the 32 bar AABA form was still more a composed song than improvisational jumping off point. 80-90 years have passed since musicians started adding extended harmony to the American improvisational lexicon, and it wasn’t until the 1950s, 60s, and 70s that odd metric grouping, completely nondiatonic harmonic progressions, and freely interpreted musical structures became a part of our musical language. This process begs a question–what was music like before? I truly can’t imagine learning to improvise music in a time when “I Got Rhythm” changes weren’t robustly investigated and developed, or Wayne Shorter’s enigmatic chord sequences weren’t available for study and improvisation. I’m sure, however, that the average music student in the 19th C. wasn’t aching for open-ended improvisational frameworks. The concept and vocabulary had to be invented, and only over time were these new inventions made available and worthwhile to rank-and-file musicians and listeners. What will the next thing like that be? What will as-yet-to-be-born musicians be working on in their teens that we haven’t even thought of, or tried, or recorded, or published, yet?

Sometimes musicians in jazz refer to “new standards,” usually meaning pop songs composed since 1970 that have become fodder for the kind of jazz-y scrutiny Tin Pan Alley and Hollywood favorites got in the mid-20th C. While some of these do present interesting structures and invite new kinds of improvisation, they are made of the same bones (pulse-based periodic song form, chord changes + melody) as the original jazz standards. I’m sure we can find some truly new materials to develop in to a set of legitimately “new standards.” My hunch is that these new materials will come from the same stuff we use and mess with already every day–computers–and that’s good. All day I and those around me are handling, using, needlessly consulting, and talking about digital stuff. In Charlie Parker’s time, the radio and popular recordings occupied some of the same space and guess what, when you hear “I Got Rhythm” 1,000 times in a year on the one or two radio stations that represent your media stream you might just learn to do something new with it. Thus with the digital age–spend enough productive time with your computer and you might come up with something good. The questions for musicians are: what is the next good thing? How can we develop and systematize something fresh? Why?

 

I am really looking forward to leading gigs at Blue Whale April 22 and May 13. The opportunity to play new music at a fabulous venue is welcome and well-timed; it’s been a few years since I’ve composed for an improvising ensemble. What a great chance to try some of the ideas I have explored in my works for solo bass, computer and bass, and as a theorist of improvisation! I am honored to be working with pianist Bevan Manson and percussionist Brad Dutz, two immensely talented musicians.

For the past month or so I have been finding my way to the kind of creative space I want us to work with. This instrumentation is familiar to me from my work with New Power Trio, and this familiarity allows me the luxury of using some tried-and-true ideas. Sadly, however, this composer’s process is always fraught; ego and ambition fight with better instincts, and I struggle to find the right modality and attitude to bring to my work desk. An entire career of successes, failures, inadequacies, grandiose schemes, and irrational expectations was examined before even one note got composed.

Now I feel sure enough to explain my creative position and goals for these concerts. To evolve as musicians I’m convinced we must also evolve new ways of envisioning, rehearsing, and discussing music. Same process usually yields same results; better incrementally but similar, maybe even stuck. I love this piano trio format and want to find new ways of experiencing it. Similarly my love of jazz improvisation spurs me to find new frameworks for tonal and/or pulse based and/or interactive improvisation. Our first rehearsal is Wednesday; I’m bringing some very fragmentary pieces, a hard-disk recorder, my bass, and an open mind.

We’ll play “free” mostly–I wonder what will happen. Even an initial rehearsal can be complex–memories of rehearsals in the past, professional codes of conduct, and blurry expectations will be a part of the experience for all of us–will these mores, recollections, and assumptions take over the music? If so, stuck-ness wins. I hope we experience something new-ish instead.

It’s entirely possible that chance and preparation allow improvisers to make just as effective decisions in the moment as composers make during weeks of focused work. In this spirit I want to begin working with free playing and fragmentary compositions, record the results, and fine tune composed sections and improvisational frameworks from there. I also hope to have 1 or 2 interactive electronic pieces ready soon. Towards this goal I have gotten some new gear; a Mackie Onyx 820i and  Line6 FBV express USB expression pedal. Though I might not be able to really create the programs I envision–MaxMSP patches that take advantage of the extraordinary skills of musicians practiced with structured improvisation to create truly new frameworks for ensemble performance–at least I have the equipment necessary (thanks for letting me borrow stuff Michael Wilson and Ko jiro Umezaki!) and a bee in my bonnet. Below are a couple of schematics showing signal chain and outlining some desired compositional outcomes. Below that are the bits and pieces I’ll bring to rehearsal. I expect to spend the most time on rhythmic cycles–a 56 beat 7 against 4 pattern and a really neat 30-beat guaguanco–and am very interested in the ideas Bevan and Brad will come up with.

 

What is the Organic Drum Machine? The first metronome may have been a pendulum like Cuthbert Calculus’ (above); gravity driven pendulums were occasionally used for musical timing by choirmasters and others (including Bartok) starting about 1700. People have used geared machines as musical timekeepers for 200 years. This has become so common that we sometimes confuse clock time and musical time. In the past few decades drum machines have become useful, aesthetically appealing, and pervasive in many styles of music. How can technology, science, and music work together to build a drum machine that uses human time rather than clock time? The Organic Drum Machine, or ODM, is intended to be a new kind of rhythm machine. Like most drum machines the ODM’s primary function will be executing rhythmic sequences, especially the kind of layered and syncopated patterns found in groove-based music. The main difference between the ODM and currently available rhythm machines will be in how timing data is compiled and parsed. Most drum machines use clock time to regulate their sequences. In these machines 60 beats per minute means exactly that. The ODM will use algorithms and statistical models based on hard data and data-driven generalities about human periodic behavior, from musical timekeeping to respiration and beyond, to determine timing. In the ODM units like the minute will have little or no direct bearing on a sounded result and 60 beats per minute will be a starting point rather than a strict measure. This latest “Funky on the One” post is an attempt to more clearly define what the ODM is and relate its proposed functionality to my earlier posts on the topic.

My first bass teacher was Darren Solomon, and his first substantive assignment involved the metronome. “Put the metronome on very slow,” said Darren, “and play right with the click. Work on this a lot: it’s the key to being a good bass player.” And I did. First at 60 BPM, then 50, then 40, then 36, and slower, I would sit and play low Cs as right on the click as I could. It’s pretty hard to do! And it’s great for development of accurate timekeeping. Thus I and thousands of others have been introduced to mechanical time, and learned to equate it with accurate musical time.

Like many changes in music over the past two hundred years or so (the familiar mechanical metronome in common use dates to around 1816) it’s easy to forget what things must have been like before the metronome existed. Just as recording and amplification created immense changes in the portability, amplitude, and timbre of music, metronomic time created tempo external to human musical timekeepers. Along with this paradigmatic shift comes a transfer of agency–tempo in the mechanized age ceases to be an internalized feel related to people and bodies and begins to be indexed relative to beats per minute, MIDI ticks, or samples per second. Thinking about musical time is transformed completely by this shift from relative, subjective, and embodied to absolute, objective, and mechanical. While few musicians would say that perfect mechanical time is a musical ideal, and some great players eschew the metronome completely, there is no doubt that, especially since drum machines have become common use items, the ability to keep time with clocklike accuracy is a desirable skill among trained musicians, and music with a mechanical beat has become standard in many styles.

Imagine if Darren had given me the same assignment, but the goal was to play with someone else at these slow periodicities. The skills involved would be very different, and the results different too. Instead of an infallible metronome click accompanied by my variously inaccurate low Cs, both players would be playing low Cs at slightly varying tempi, each working to match the other. Issues alien to clock time would come into play-who is “right?” What is the desired tempo anyway? Why bother with this? Indeed Darren’s assignment, and much of my and many others’ training in musical time, depends completely on availability of and faith in a mechanical timekeeper.

While clock-based timing can be effective for practice, analysis, and in performance or recording contexts, this clock time is but a recently developed approximation of human musical timekeeping processes that are related to the wealth of time sensitive biological, social, and environmental activities humans and all living things undertake constantly and at varying periodicities (respiration, walking, digestion, waking/sleeping, agricultural cycles, etc). It’s easy to confuse “solid” steady timekeeping with mechanical time, but I propose that human enactment of rhythm is fundamentally different, unrelated to clock time, and at deeper levels of analysis human musical rhythm will reveal as yet unquantified types of accuracy and patterns at micro and macro timing hierarchies. New information about this aspect of human rhythm will lead to a better understanding of rhythmic expressivity.

Effective and unique rhythmic expression, even when it is “just” a well-played pulse, brings a great deal of nuance and meaning to music, yet the exact quality of such nuances and meanings are largely unavailable to the composer, music analyst, and programmer. How can we tap into this deep well of important musical information? The Organic Drum Machine (ODM) project is my proposed path towards digital rhythm derived from human rather than mechanical data sets. My main goal is to create a rhythm machine using Max/MSP/Jitter that can execute onsets using data and formulas derived from human timekeeping models. Some of these as-yet-to-be-built models will include concepts discussed in earlier posts such as metronomization, preparatory microtiming, hypermetronomic information, centeredness, and wet vs dry musical processes. I have yet to research other human timekeeping paradigms, especially long-memory statistical models, and further research is required on all fronts.

At root this project is about expanding and improving the computer rhythm machine. The ODM is intended to be used in compositional and performative contexts; it’s a creative tool. As a musician, composer, and music lover the rhythmic patterns found in African diasporic music have been a source of great inspiration and joy for me, and many musicians in these traditions from the past 30 years or so have used computerized rhythm in their compositional, performative, or recording practice. Indeed certain trends and periods in music production have been defined by the sound of specific rhythm machines (Roland’s TR 808 being a primary example), and it is hard to overestimate the impact computer rhythm machines have had on the timbre, structure, and production of many musical styles. The time has come for rhythm machines to move beyond clock time and into human time. Such a shift in focus at the programming level will invite new thinking about how, when, and why we use computerized rhythmic sequences. We are long past the time when computers had the capacity to emulate rhythm using human models of timekeeping, and are approaching a moment where computer music and human performance can be fully integrated. Clock time is no longer a sufficient tool for measurement and expression of musical rhythm.

So what is the ODM and how does it fit in? At this point I am not entirely sure. While I envision a future program that uses many algorithms to generate novel, interactive, highly syncopated, and timbrally heterogeneous rhythmic textures related to the Brazilian, West African, and Cuban percussive traditions I enjoy, my initial plans are not nearly this ambitious. I hope to expand the functionality, nuance, and temporal palette of rhythm machines starting with the “simplest” of concepts: a steady pulse. Computer generation of a steady beat that is not clock based is not to be trifled with. How do we do it? How and why does a person clapping a pulse deviate from clock time, and, if they are clapping along with someone else, how will their patterns differ from and influence each other? What happens when two percussionists play even a basic interlocking groove? These questions are remarkably deep, and while I do not plan to solve them in a definite way, the ODM project addresses such issues.

As I have worked on these posts I have made some decisions about what the early generations of the ODM machine should accomplish:

1) Maintain a “steady pulse” without adhering to clock time.

Two analyses of improvisational microtiming (Benadon 2009, 2007) and a computer emulation of grooving rhythms (Wright and Berdahl, 2006) tended to focus on sub-tactus onset events. In these studies the tactus was either assumed to be clocklike (Benadon 2009, 2007) or a mechanical click was utilized (Wright and Berdahl 2007). These studies sought to parse, contextualize, or emulate expressive microtiming at the sub-tactus level by necessarily ignoring the fact that any performance not utilizing mechanical time will include temporal changes (speeding up, slowing down, changes in emphasis, etc) at the tactus, metric, phrase, and section levels.

The ODM, in contrast, is meant to generate onset events using human-like timing data at every level of a rhythmic hierarchy, and as such a first step is to develop algorithms that execute a periodicity without needing to return to unison with some clock-based timing unit.

Most rhythm machines have various “swing” and randomness settings that can be used to vary the onset events of a particular programmed sequence. These settings function by departing from then ultimately returning to a fixed mechanical time, for instance quarter note = 60 BPM. The ODM will be fundamentally different from other rhythm machines. For example, it is possible in many rhythm machines to set up an eight measure rhythmic pattern in 4/4 time with a metronome marking of quarter note = 60 BPM that uses swing and randomness settings to generate a sequence of unevenly (“expressively”) executed interlocking or overlapping patterns. However, this sequence will last exactly 32 seconds no matter how extreme the swing and randomness settings may be. With the ODM such patterns will last about 32 seconds, but the timing information will, like human performance, “naturally” speed up, slow down, and never have to snap back to a clock based temporal grid. At first I plan to err on the side of inaccuracy such that onset events can be made to sound more ragged than a human timekeeper, and work towards varying levels and kinds of human accuracy. This first step of maintaining a steady pulse will be followed by efforts to emulate some kind of basic two part rhythmic pattern such as clave and tumbao, walking bass and high-hat on 2&4, etc (see #3 below).

2) Execute rhythms for extended periods that vary in ways similar to human rhythmic variance (for example, as found in musical timekeeping, stride, and pulse rate cycles).

Studies of human gait dynamics reveal that “long-term fractal dynamics of the stride interval are normally quite robust, they are apparently intrinsic to the locomotor system, and they exist at a wide range of gait speeds” (Hausdorff 2007, 561). Self-similar gait patterns repeat over very long periodicities into thousands of strides (Hausdorff 2007, 560). Similar fractal patterns and scaling can be found in human respiration (Peng et al. 2002). In the fields of biomechanics, medical dynamics, and music, variation from a mean rate (be it a rate of pace, pulse, or musical rhythm) can easily be misidentified as statistical noise. In many fields this noise is being reevaluated and considered a key aspect of biomechanical function. How can this new data be incorporated into materials for creative musical work? In scientific terms maintaining a musical pulse is “isochronous serial interval production” (ISIP), and recent studies suggest, as I do, that “ISIP is a more complex process than is assumed by influential timing models and theories, and that realistic modeling of human timing must account for nonlinear variability patterns” (Madison 2004, 105).

While best practices and data are still to be determined, the ODM should generate note onsets using various human timekeeping algorithms, emulating the long term patterns found in phenomenon such as walking, breathing, and musical timekeeping. It is common for rhythm section players in groove based music to play thousands of notes in a single piece, and the data that dictates the ODM’s note onset events will reflect the ways in which we maintain rhythm over such large numbers of events. Will such a shift yield a perceivable effect for music using machine rhythm and drum machines? How can such a change influence composition and rhythm programming?

3) Execute predetermined two or three part syncopated patterns, each part of which is buffered from the other parts, and maintain these rhythms in a human way.

This task requires further development of algorithms that emulate human timekeeping tropes. In a 2001 experiment Yanqing Chen et al. examined ISIP intervals for participants tapping a computer key in phase with and syncopated against a sequence of mechanical beeps. As with Guy Madison’s 2004 study, the results showed a clear pattern of long-memory influence on ISIP intervals and a successful fractal modeling of the phenomenon (Chen et. al. 2001, 4). In addition, Chen’s experiment revealed that the individuals studied showed a longer statistical memory in their syncopated tapping patterns. In other words, tapping along with a pulse and tapping syncopated against a pulse involve different methods of timekeeping. Theoretically, statistical models could be built that reflect how people maintain various kinds of interlocking patterns to form grooves, and these models could be used to supply timing information for a rhythm machine such that patterns that use triplets involve triplet syncopation statistics, patterns based on 2:3 clave will use 2:3 clave based statistics, etc.

Entrainment is both a biological and creative phenomenon; we are entrained by circadian rhythms and become entrained by effective musical rhythms. What is less clear is how the diverse multilayered hierarchical rhythm styles found in music affect our brains differently, and how musicians in various traditions use their brains differently to keep time (become actively entrained) in interlocking rhythmic patterns. One thing is sure: musicians have to listen to each other to make their rhythms fit together properly. This involves coordination of internal processes with ongoing auditory input. Various studies (for an overview see Janata and Grafton, 2003) have shown that metrical grouping and familiarity with the rhythmic traditions being referenced are key to a musician’s ability to process multipart rhythmic structures. While it’s probable that musicians in an ensemble share a common sense of metrical grouping and style, each musician playing a groove must also, while keeping their pattern, adjust to their collaborators’ patterns. The ODM’s architecture should incorporate aspects of this process, and its timing adjustments will necessarily be buffered at various intervals so that these adjustments are not too quick. As a bassist I sometimes feel that I focus primarily on my part, assuming that my internal clock and those of my fellow musicians are well adjusted enough to be in sync. It’s only when something notable happens (someone is drifting temporally, or inviting interaction) that I adjust my own playing. I imagine a similar programming attitude; when executing a multipart rhythm, the ODM should selectively “listen” and only adjust when a groove’s onset intervals have crossed some threshold of accuracy. Ideally this will lead to mechanized rhythms that reflect our own processes of entrainment as performers, and yield grooving rhythms with a fresh sound.

4) Accent beats both with intention (in the “right” place) and human-like inaccuracy (a little louder here, a little softer there).

Accents can be created by patterns of emphasis and de-emphasis within a musical texture or supplied by a listener based on their own expectations (Grahn and Brett, 2007). They perform a key function in hierarchical grooving rhythms; various on- and off-beat accents allow listeners and performers to find the unique “pocket” central to a rhythmic pattern. While initial models of the ODM will not be programmed to algorithmically generate new rhythmic structures and accent patterns, this is a future goal, and towards that end some gestalt for organizing accents should be developed. The ODM’s initial sequences will be based on familiar patterns such as the Brazilian maracatu or Cuban guaguanco, and beats will be accented using the tropes standard to these traditions. I wonder if a systematic study of diverse hierarchical rhythms might reveal some common accent locations and ways of emphasizing syncopations. Such data could be used to build algorithms that allow future iterations of the ODM to accent certain beats appropriately (i.e. in a culturally informed human way) as it generates novel rhythmic structures.

In addition to stylistically informed intentional accents the ODM should also emphasize and de-emphasize onsets just as human timekeepers do. While jazz drummers tend to have very well-developed and accurate ride cymbal patterns, these patterns must have a constant flux in accent and amplitude, each onset slightly different, some intentional and some simply human. Are there long-memory patterns in accent production as well as onset timing? How can this data be gathered and used in the ODM?

5) Maintain these predetermined patterns with less and similar accuracy compared to human timekeepers.

I imagine a process where human timing data is collected (either from previous studies or new ones) and algorithms are designed to emulate this data and trigger note onsets in the ODM. However, rather than working from clock time towards human time, I plan to approach the process in the same way a photographer puts an image in focus, spending time with pulse patterns that are too inaccurate (out of focus) to be considered musical, then making these patterns successively more human (focused) in accuracy. Metronomic time has become a very taken for granted standard of timing for onset events, especially in computer-based music, and initial goals include avoiding this kind of mechanical ISIP as the concept is developed.

Here are some things that the early generations of the ODM machine should NOT accomplish:

1) Interact with human performers or other programs.

Real-time interaction is absolutely a future goal of the ODM project. For the time being, however, my work will focus on the ODM’s rhythm generation software. While I hope that early generations of the ODM will be useful for live performances and recordings (a next step forward from my 2009 piece Descarga), these initial musical applications will not incorporate computer listening, onset detection, etc.

2) Generate new rhythms, compose, or improvise.

Algorithmic improvisation is another long term goal of the ODM project. At first the programming will be devoted to the problems associated with generating human-type ISIP rather than how to get a computer to compose convincing hierarchical rhythmic structures.

3) Communicate with other software platforms.

A rhythm computer able to play rhythms with this type of non-clock based algorithmic data would indeed be a new kind of sequencer. Integration of this kind of program into existing platforms (MIDI, DAW, etc) would require a fair amount of data translation. Good music software plays well with other music software. The ODM is intended to be a creative tool and should at some point be able to “plug in” to existing programs; for now this is not a priority.

And these are notably unknown:

1) Sample, synthesis, or hybrid sound realization?

It’s possible that the ODM program could simply send timing data to an existing drum machine. I hope that the timing program and sound synthesis will both take place in the Max/MSP/Jitter environment. All details are still to be determined.

2) Based on what “hard data” about human timekeeping?

Gathering highly specific information about rhythmic timing presents several difficult questions: What is a note onset? When can is be said to be “perceived;” at its exact beginning, when the player hears it, later when other musicians hear it, etc? Is the onset itself a germane piece of timing information or is a musical beat felt at some point before or after an attack? While there are many studies of ISIP among musicians and non-musicians, this data is often based on subjects’ computer key-clicking or drum pad input. Real musical timekeeping rarely uses either of these interfaces. Is there existing useful data measuring ISIP for musicians striking piano keys, cymbals, hand drums, plucking strings etc?

The ODM project may become a data gathering project at first–unlike some scientific studies my goal is to use data from musicians who are actively performing and parse this data using a certain amount of stylistic correlation–and this data might not yet exist.

3) Types of statistical, algorithmic, or random timing formulas.

Examination of available and new data on human musical ISIP and other periodic behavior should help determine the best modeling formulas. How different ways of crunching numbers affect the resulting patterns will be interesting. Many listeners, myself included, have become accustomed to hearing and enjoying various kinds of machine rhythm. Many theorists and composers have used statistical modeling in their musical work. The ODM will bring this kind of mathematics to bear on the sequencing of rhythm, and in the process my collaborators and I will have to learn to hear how fractal dynamics sound in various rhythmic cycles. I hope the ODM will be an aesthetically successful addition to the already well-developed field of rhythm sequencing.

This series of posts has examined various aspects of human timekeeping and musical examples that problematize the accepted notion that effective execution of rhythm in groove based music is essentially or ideally metronomic. My goal is neither to debunk the consensus idea of what “good time” is nor to decry the prevalence of mechanical rhythm. Rather, I hope to expand the functionality, nuance, and temporal palette of rhythm machines.

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References

Benadon, Fernando. “Time Warps in Early Jazz.” Music Theory Spectrum, Vol. 31, No. 1 (Spring 2009), p. 1-25.

Benadon, Fernando. “A Circular Plot for Rhythm Visualization and Analysis.” Music Theory Online, Vol. 12, No. 3 (September 2007).

Chen, Yangqing et al. “Origin of Timing Errors in Human Sensorimotor Coordination.” Journal of Motor Behavior, Vol. 33, No. 1 (2001) pp.3-8.

Grahn, Jessica, and Matthew Brett. “Rhythm and Beat Perception in Motor Areas of the Brain.” Journal of Cognitive Neuroscience 19:5 (2007),  pp. 893–906.

Hausdorff, Jeffery M. “Gait dynamics, fractals and falls: Finding meaning in the stride-to-stride fluctuations of human walking.” Human Movement Science 26 (2007) pp. 555–589.

Janata, Petr, and Scott T. Grafton. “Swinging in the Brain: Shared Neural Substrates for Behaviors Related to Sequencing and Music.” Nature Neuroscience Vol. 6, No. 7 (July 2003) pp. 682-687

Madison, Guy. “Fractal Modeling of Human Isochronous Serial Interval Production.” Biological Cybernetics 90, (2004), pp. 105–112.

Peng, C. -K., et al. “Quantifying Fractal Dynamics of Human Respiration: Age and Gender Effects.” Annals of Biomedical Engineering, Vol. 30 ( 2002), pp. 683–692.

Wright, Matt, and Edgar Berdahl. “A Survey of Computer Systems for Expressive Music Performance.” Final paper, Stanford Machine Learning (CS 229), Dec 2005.

The previous post in this series discussed concepts of microtiming in groove based and improvised music. While models for the analysis of sub-tactus onset events and methods for examining such events at the phrase level exist, such approaches are in fact more forensic than generative as they do not engage with preparatory microtiming, the embodied process of initiating a note onset event. My goal is to create an organic drum machine with Max/MSP/Jitter using algorithms that elevate the human reality of musical timekeeping above the clock-based fantasy model of rhythm found in current drum machines. This task presents unique problems at every hierarchical level of rhythm from microtiming to form. Given that human timekeeping employs various brain functions, constantly refers to the surrounding musical environment, and relies upon complex brain/body/instrument relationships for expression, a truly functional organic drum machine would require significant amounts of data gathering using human performance models to build effective algorithms.

Such data could not be taken from existing recorded performances; these too would be forensic rather than generative, and the goal is to make the organic drum machine a human-like generator of rhythmic patterns. Ironically, it seems that some data should be created in controlled settings where musicians are recorded playing along with a mechanized rhythm of some kind (how else can the exact location of the tactus be known?). Such performances could be analyzed and the data used to implement appropriate formulas for the machine to use. But how should such analysis be conducted? What is the relevant data? Individual musicians respond to and enact musical timing differently; we all have personal ways of understanding, expressing, and adding to the rhythmic texture of music during performance.  The performer’s perspective on and concept of his/her own process should be included in this analysis. In this way information pertinent to the enactment of rhythm can be reinforced and understood from the player’s point of view. Much of my current work explores the boundaries between individual and shared musical understanding, and in my composing, programming, and improvisation I seek to discover how my brain processes and remembers music towards the goal of creating a truer personal expression of music. Research leading to the development of an organic drum machine presents a rich forum for investigation of the simultaneously disparate and common narratives, strategies, and emotions we express through rhythm, and an opportunity to create computer-based music that is deeply rooted in human methodologies.

The following 10-second excerpt from my composition Descarga (3:50-4:00) presents several rhythmic events that further complicate concepts discussed in this series:

Download audio file (descarga_excerpt.mp3)

At this point in the piece the computer had just finished transitioning from one rhythmic cell, called “jr,” to another one called “2gin.” While the rhythms played during the transition (not excerpted) let me know we would arrive at 2gin, I had no way of knowing which exact 2gin mini-composition would start at the downbeat where this excerpt begins. For each rhythmic cell in Descarga there are two or more possible sections the computer randomly chooses to play, each with a different set of patterns and groupings. As it happens the computer and I ended up playing more or less in unison. 2gin is a seven-beat rhythmic cell based on 3:2 rumba clave. In this mini-composition the 2gin pattern is introduced by 14 repetitions of a dotted-eighth/sixteenth type pattern. In the excerpt above improvised bass and sampled percussion sound a set of five more-or-less unison pairs of dotted-eighth/sixteenths. During the next nine beats the percussion holds steady as the bass pattern wanders around, finally returning to almost-unison with the percussion’s last two notes.

Taking a more detailed look, the percussion pattern alternates between two sounds-low and high-and never deviates in its pattern or order of pitches. While my improvised pattern does deviate away from unison with the percussion, the bass pattern maintains a similar low-high (A-E) gesture. In the last half of the excerpt 5 low-high bass patterns occupy an indeterminate rhythmic space more or less unrelated to the computerized percussion pattern. The bass finally returns to match the percussion pattern, but now the bass pattern is reversed high-low in relation to the computerized percussion’s low-high. The overall effect, while certainly disjoint in the last half, ends in a way that implies I knew how to end my wandering two-pitch pattern and was intending to reverse the order of pitches in relation to the computerized percussion: The computer pattern and my improvisation both end on the downbeat and pause. However, I know that during the improvisation I was not able to keep track of how many computer percussion patterns were elapsing as I varied my rhythm. While this excerpt represents an improvisational and rhythmic success for me–a cool idea that worked out well–the mechanisms I used to pull it off were far from surefire.

Chart 1 shows the timing of all the onset events in the excerpt. Time progresses from left to right. The lower two symbols represent the computer percussion (beats and upbeats, on a grid), while upper symbols show the two-pitch improvised bass pattern. At first the bass’ pattern closely matches the computer both in rhythm and gesture: low pitch on the beat, high pitch on the upbeat. The bass beats are almost simultaneous with the percussion beats while  the upbeats slightly anticipate the percussion upbeats, swinging the rhythm. This initial unison effect establishes two relationships between improvisation and computer rhythm: first that they are rhythmically similar, second that low and high onsets mark the same type of beat subdivision. At beat six the improvised line begins to depart from the computer’s pattern. While the bass and percussion patterns grow apart, the beat/upbeat low/high relationship allows the bass pattern to maintain a sense of gesture so that when the final pitches of the bass are low-high against the percussion’s high-low expectations established in the first half of the excerpt are defied.

Transcription of these onsets presented a challenge. The sense of gravity created by both the computer percussion and elastic bass pattern invite various divergent notational strategies. In addition low frequencies and room sound reflect and muddy the attacks. I finally chose to use a close-mic recording of the bass performance, slowed the excerpt to half tempo in Max/MSP, filtered the audio to emphasize the bass’ overtones at close to 880/1320 Hz, and used pro tools to locate transients near audible onsets. The animation shows how the pattern unfolds. Analysis reveals that although the bass’ pattern in the last half of the excerpt does present certain rhythmic relationships to the computer percussion what I am mainly doing is slowing my pitch A beat pattern (chart 2). In addition, with each ritardando low-high cycle I am shifting the location of E, the high pitch, so that it is more “centered” within the the low A inter-onset interval (IOIs) around it. As my pattern slows, its rhythm also becomes more evenly subdivided (chart 3). (all data from analysis here)

Careful analysis plus my perspective as the performer leaves little doubt that the pattern in the last half of the excerpt is gradually slowing in relation to the computer percussion. I am ignoring the accompaniment rather than playing off of it. However, as I successfully slowed down and shifted the upbeats in my improvisation, I’m sure I lost track of where I was within the longer metric system. No doubt I knew it was the seven beat 2gin pattern, and this percussion pattern would soon be expanded to include other groupings, but in the last five seconds (if not longer) of this excerpt I am lost and don’t know when beat 1 will return. The final pair of bass pitches that return to (almost) match the percussion and invert the low-high pattern are stumbled upon rather than intentionally played with the downbeat of the 2gin pattern. As I slowed down my As-Es I could hear percussion patterns passing by but was not counting them. The final two bass pitches in the excerpt, also the most closely spaced, show how I was brought out of my meandering reverie. The final low A follows the slowing down trend, but immediately after it I hear the computer percussion’s high pitch and, as quickly as possible, (so quick my onset is early) play my last high E so it matches with the reliable percussion pattern that, even without counting, I know the shape of. This could easily have happened three beats earlier when my A also aligned with the percussion or, after the percussion momentarily stopped (right after the end of the excerpt), I might have continued playing. In hindsight these possible outcomes seem less elegant than what I did play.

I was lucky to end up back with the percussion. In the seconds before that happened I was ” throwing darts,” playing kinda slowing down and strangely subdivided pitches that landed somewhere within the accompaniment, and the processing required to play those pitches made me forget where 1 was until something (that last high drum) snapped me back into lock step with the rhythm. If I’d stopped two beats earlier that might have sounded incomplete. If I’d continued playing when the percussion stopped who knows what would have happened. This excerpt, although not an example of a grooving pattern, does contain the kinds of complex “binary” (like high and low pitches in bell patterns) rhythmic data I enjoy working with as a composer, and does relate in some ways to the kinds of patterns I hope to create with the organic drum machine. While this analysis does not bring me much closer to a set of guidelines for analyzing human rhythmic performance, it does present some new concepts for me; the idea of shifting a note’s “centeredness” within other notes, faster or too fast reaction times at clutch moments, and the possibility that recorded rhythms which in hindsight sound well thought out may owe more to chance than extra-nimble musicianship.

Microtiming formulas will clearly be a significant part of the organic drum machine’s programming. It’s possible that phenomenon discovered through controlled study of performances patterns of microtiming can be indexed to performers’ own sense of their rhythmic expression, and, by extension, lead to distinctly human ways to program in the digital environment. Other timed natural processes, like plant growth, stride pace, respiratory rhythms , etc, show self similarity and cyclical patterns when studied at different time scales. Perhaps human timing in music contains self-similar patterns at various temporal levels as well. The next post in this series will make a clearer definition of what the organic drum machine should do and establish basic goals for the project.

I studied string bass with Mike Richmond, an extraordinary bassist and teacher, for two years in college. During our first year together I was struggling to develop swinging eighth note “lines.” He would improvise with a swing feel and sound amazing. I would try, and sound robotic at best. The situation seemed unsolvable. Then, one day, I got it. I can clearly remember suddenly being able to string eighth notes together in a more or less jazz style. There was a distinct “a-ha” moment when my brain started to think in jazz phrases and was able to express that thought through my body/bass interface. It was a breakthrough. How did this happen?

Something must have changed in my kinesthetic ability to allow such progress. This change is an example of improvement at the level of expressive microtiming. My poorly swinging eighths suffered from two faults; a lack of overall precision (so that “even eighths” were probably herky-jerky) and an inability to execute the subtle explorations of beat subdivision intrinsic to jazz improvisation (so that “swinging eighths” probably didn’t swing). Both these problems involve very small adjustments in timing: my bad swing feel was poor microtiming and my better swing feel is expressive microtiming.

This series of posts seeks to develop theories and concepts leading to development of an “organic drum machine” using Max/MSP/Jitter. So far I have theorized that players in groove based music often metronomize patterns that are syncopated in relation to their instruments’ primary rhythmic role, and proposed that the organic drum machine program should enact rhythm without strictly adhering to clock time, be capable of speeding up and slowing down as people do, and incorporate hypermetronomic rhythmic information as part of its design. Effective microtiming is key to all these rhythmic events: metronomization in relation to a contrasting pattern requires smooth adjustments to the location of that pattern, grooves that run on human time will shift a bit from clock time with each beat or metric grouping, and musical speeding up and slowing down involves matching a pattern to the surrounding musical texture. One great strength of computer rhythm machines is their ability to accurately execute rhythms at any time scale from micro to macro. How can this strength be brought to bear in computer generation of grooving patterns? A first step is to find an analytical method that describes the real time performative enactment of rhythm in groove based music, as this will provide data to incorporate into new rhythm algorithms. Phenomenon such as the swinging eighth note can be  studied through sub-tactus level analysis of pitch onset events (Benadon 2007). Such analyses are illuminating but do not engage with preparatory microtiming, the musical processes leading up to moments of actual musical sounding, and as such represent a post-mortem accounting of real-time music making rather than an investigation into the generative elements of improvised performance.

Effective microtiming includes a such a multitude of deviations from idealized beat subdivisions that truly wonderful and truly lousy microtiming can live in the same temporal locations. Though it is possible for listeners to identify very small discrepancies in division of the tactus an analysis of such divisions does nothing to clarify the connections between a musician’s choice in timing and expressivity. Further, this kind of analysis will result in similar results for both great and mediocre improvisation; all musicians deviate from clock time to some extent in their playing. Correlation of phrase level gestures with analysis of note onset intervals (as in Benadon 2009 and many other papers) sheds some light on an improviser’s process and problem solving strategies but ignores the fact that before an audible note onset occurs the improviser has already made their choices regarding pitch and rhythmic placement.

So the organic drum machine, while it must incorporate extensive and flexible deviation from idealized beat locations into its algorithms, should not strictly rely on a model of effective microtiming provided by analysis of preexisting music. In reality the audio information we hear in live performance or recordings, though it is filled with remarkable rhythmic information at every temporal level, is the result of an inaudible process of musical enactment, one level of which is the preparatory microtiming mentioned above. Let’s call the inaudible processes involved in preparation for musical sounding “wet” and those we hear “dry.” Wet processes are internal, chemical, biomechanical, and personal. Dry processes involve active creation of sound; singing, striking, bowing, blowing. Each audible dry process has a pendant wet process that precedes it. While analysis of recorded artifacts reveals the microtiming of dry events, wet events are left out of the picture. The organic drum machine should base its choices in microtiming on some kind of wet process model. For the organic drum machine, as for the real performer, dry process is about release of sound. It’s the wet process that truly dictates when and how that sound is made.

Picture a dartboard and a person preparing to throw a dart at it. All the preparation for throwing is wet process; selection of target, grip, windup, etc. Once the throwing motion has  started dry process begins, and by the time the dart leaves the thrower’s hand there is no way to change the dart’s course or destination; the flight of the dart is all dry process. Each step in this procedure takes some amount of time and is controlled by the thrower, but the time the dart is in flight and the short period of time the dart takes to penetrate the board represent the results of a wet process rather than the process itself. Production of musical sound is similar; though some instruments allow continued input after an onset event, the moment of audible sound onset occurs after the wet process of preparation and release and as such its timing is a result of preparation before the onset event. Is it possible to study preparatory microtiming? Current studies of music perception and performance show that music engages diverse areas of the brain. How do these brain regions function in the milliseconds before a musician triggers a note onset? How do individual musicians execute these rapid thoughts and movements in similar or different ways?

Looking again at our dartboard, while the dart player intends to land a dart in some specific location (such as the bulls-eye, or the outermost 20 wedge),  the wet process may produce a different resulting dry process (a total or close miss). Further, each zone of the dartboard has its own subvidisions. The top left corner of the outermost 20 wedge and the exact center are different points, yet both are in the same zone. Then there are game strategies: given the same or similar score different dart players will choose different targets. Unless a player announces their intent observers and fellow participants can’t be 100% sure whether a thrown dart found its intended location. Finally, there are different rule sets and games that use the same dartboard. To understand a dart player requires knowledge of both his or her strategy and the rules of the game.

Like a thrown dart, the wet process leading to a note onset can (and often does, especially in improvisation) result in an event that is close to but not quite exactly at the temporal spot the performer intended. An onset event can be located at various times within a temporal “zone” like the dartboard’s bulls-eye or outermost 20 wedge, yet still have a similar effect (like swinging eighths, each pair slightly different in MS duration, but more-or-less tripletized). A musician’s strategy also comes into play; how does he or she intend a particular onset to work at the phrase level? How will he or she adjust if their wet process leads to unexpected results? Stylistic rules play a prominent and changeable role in the enactment of rhythm and phrase. At what level can the listener or fellow musician truly understand the rules governing a player’s rhythmic impetus? Is the performer even aware of how cultural, situational, personal, and stylistic rules are being integrated into their preparatory microtiming?

Programs that are designed to create groove based patterns, like the organic drum machine, will benefit from a perspective that incorporates the embodied aspect of rhythmic performance, recognizes the relationship between wet and dry process, and uses human data to inform timing at every metric level.

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References

Benadon, Fernando. “Time Warps in Early Jazz.” Music Theory Spectrum, Vol. 31, No. 1 (Spring 2009).

Benadon, Fernando. “A Circular Plot for Rhythm Visualization and Analysis.” Music Theory Online, Vol. 12, No. 3 (September 2007).

Iyer, Vijay. “Embodied Mind, Situated Cognition, and Expressive Microtiming in African-American Music.” Music Perception, Vol. 19, No. 3 (Spring 2002).

Guy Madison, Lea Forsman, Örjan Blom, Anke Karabanov, Fredrik Ullén. “Correlations Between Intelligence and Components of Serial Timing Variability.” Intelligence, # 37 (2009)

Dmitri Shostakovich’s 103 birthday was September 25. A great composer, his works are beautiful, majestic, ironic, clever, and sometimes all these things at once. A devout Russian, Shostakovich was by turns Soviet poster boy and target of Communist party ideologues. This birthday reminds me of Leonard Bernstein’s televised Young Peoples Concert from  January 1966. Titled A Birthday Tribute to Shostakovich, the concert celebrated Shostakovich’s 60th birthday and featured performance and analysis of the composer’s 9th Symphony in Eb major. (Bernstein’s Young People’s Concerts are amazing. Pricey, but worth it and published by Kultur)

At the time of this televised concert the US and USSR were deep into Cold War politics. These politics were apparent at every level of social discourse, and tension between the two nations was expressed in the arts in many ways. While the US and USSR would engage in cultural exchanges, these events were highly politicized and prone to diplomatic intervention. (For example, a few months before Bernstein’s Birthday Tribute to Shostakovich was televised, a Moscow production of Hello Dolly was canceled as a result of Communist party pressures.) Shostakovich was famously reprimanded by Stalin on two occasions even as his works were critically acclaimed internationally. The composer feared for his life and family, lost professorships and professional opportunity as a result of party opinions, and generally had to adjust his music and speech to conform to fickle party standards. As a result, when Shostakovich visited NYC in 1949 and 1958, ostensibly as a Communist Party spokesman, his messages were effectively overpowered by the (often CIA funded via The Congress for Cultural Freedom) anti-Soviet arts community in New York, led by composer Nicholas Nabokov and others, who used stories of his persecution as an example of Soviet injustice and asked him to defect.

For Bernstein, an astoundingly successful and roundly acclaimed artist, the politics of the Cold War were somewhat different. With family roots in Poland and the Ukraine, Bernstein felt an urgent desire to bring the US and USSR into better relations. He took the New York Philharmonic to the USSR in 1959, and in his writing frequently called for improved communications between the two countries. His ties to Russia are deep, as not only his family but his mentor Sergei Koussevitzky were identified with Russian culture. While Bernstein was in a position to speak his ideas freely and even serve as a catalyst for change, the realities of his role and the experiences of his collaborators did limit his speech. Charles Dubin, an original director and producer of the televised Young People’s Concerts, was blacklisted shortly after the series premiered in 1958. In Bernstein’s preparatory notes for the Birthday Tribute to Shostakovich (available at The Library of Congress’ website) the conductor, given the bully pulpit of live national broadcast, prepares an opening statement that directly confronts Shostakovich’s struggles as a Russian and a Soviet:

And what is the first and main quality of this Russian man? His Russian-ness. Shostakovich’s devotion has not only been to his art, but also to his country…He is a very patriotic man, but he is also an artist, and that combination has sometimes gotten him into hot water with the people who guide the very revolution in which he grew up. (see document linked at left, quoted section is near center)

But this kind of direct engagement with Shostakovich’s personal story was not appropriate for The Young People’s Concerts, or, really, for a birthday tribute. Even as the pointless cloak and dagger of the Cold War played out for all to see (or infer, or fear), Bernstein was not in a position to outline Shostakovich’s uncomfortable lot in life. While Bernstein clearly was ready to speak about the topic on national television (thus the initial notes reproduced here), something made him change direction. Whether it was a personal choice, network naysayers, or (more likely in my opinion) the overarching vision of “pure music” presented by The Young People’s Concerts, the broadcast television program presents a  jolly tour through some of the humorous references and musical tricks in Shostakovich’s 9th Symphony. As Bernstein writes in a later draft “birthdays should be gay. Hence avoid the serious, heavy, patriotic aspects and emphasize the merry.” (see document linked right, scribbled at top) Bernstein was able to quell his famously outspoken manner in order to give the composer due honor in television style. What better happy birthday could there be given the circumstances?

So I also say happy birthday Dmitri Shostakovich! Let his ability to express great ideas through music (sometimes under duress; often with skillful subterfuge and subversive wit) inspire and remind us that compromise is a part of the creative process.

This post is based on a longer article,

Leonard Bernstein’s Young People’s Concerts:
A Birthday Tribute to Shostakovich in Context

click above for full text and references.

I had just decided to study music for real when I began to study bass with Ron McClure. A great bassist and pianist, McClure would play piano in our lessons. His first assignment; learn to internalize tempo. When we tried to play a tune (it was “Alice in Wonderland,” a waltz by Sammy Fain) I clearly did not know how to handle a simple 1-2-3, 1-2-3 count-off. “You gotta be able to take a count-off!” McClure rightly pointed out. I have since learned this skill, more or less, but the complexity of the task is notable.

Keeping a simple beat is at once the most elementary of jobs and a window into the only vaguely understood cognitive process of musical timekeeping. A standard jazz count-off, half notes verbally counted 1, 2; then quarter notes 1, 2, 3, 4, is two measures long. At faster tempi this is sometimes counted as whole notes 1, 2; then half notes 1, 2, then quarter notes 1, 2, 3, 4, and totals four measures. These count-off patterns are notable first because of their brevity; musicians are expected to adjust their sense of musical time for long repeated forms (jazz pieces are often more than 300 measures long) based on a count-off unit which is only 1/16th the length of a standard AABA form, and a tiny fraction of the whole piece. In this context tempo, such a key aspect of a piece, is (in part at least) determined by a count-off so short it can be compared to measuring a football field in inches rather than yards. There is also the quickness with which musicians internalize and express these brief count-offs as temporally regulated music. Even if a musician ends up playing a bit faster or slower than the count-off tempo, they have still accomplished a remarkable feat of cognition by taking a 2 or 3 second count-off and turning it into a real time reflection of the counter’s intended speed.

One analogy for this is the carpenter’s level. A short six inch level (like a short count-off) doesn’t give an accurate sense of a surface’s relationship to the “level” ground below. Carpenters use 3 foot or longer levels to see if something is straight. While the ability to maintain constant tempo based on prompts like the count-off is considered a basic skill for musicians, it’s pretty unusual for a count-off to be as long in time as an accurate level is in length, though it seems such a practice would result in a better, more “level” relationship between intended and performed tempi.

This analogy overlooks an important point: The carpenter’s level is a mechanical device used to show the relationship between an object’s surface and gravitational pull (a fixed quantity), whereas human musical time is a neurological process which is expressed using mechanical means such as instruments and the voice. There is no ideal fixed (“level”) quantity in musical timekeeping. While it is possible to compare a live performance to a metronome (this will happen later in the post), real timekeeping in music isn’t  directly comparable to mechanical time; it’s apples and oranges. The human ability to keep time is a phenomenon influenced by personal, social, and cultural rhythmic preferences, not mechanical forces such as gravity or clock-like ticking. While the metronome is definitely useful and has been around since Beethoven’s time, it is a tool for practice and reference, not suitable for deep analysis of musicians’ in-the-moment rhythmic expression. But there must be some way to understand why some performances are so engaging rhythmically. We can all think of music that is rhythmically “good,” and musicians often describe another player’s pleasing rhythmic skills as “good time.” What is “good time?”

This question is often mixed-up with a related but different question something like “does [person] have good [i.e. metronomic] time?” While the ability to keep time with clock-like accuracy, especially in improvisational or groove-based music, is a sign of exceptional musicianship, this ability is only related to the idea of good timekeeping in music. Saxophonist Bob Parsons, another teacher of mine, once described “good time” as a player’s ability to rhythmically invite both listeners and other players into the musical texture. While there must be a basic level of rhythmic continuity (principal beats more or less equally spaced in time) for a groove to be perceived, it is the intent of the performed rhythm, not its relationship to mechanical time, that really makes for “good time.”

As I compile ideas and guidelines for developing an organic drum machine, it’s significant to realize that “good time” and metronomic time are two rather different concepts. While all computers parse time in a clocklike fashion, it must be possible to create data sets and algorithms that go beyond standard drum machine swing and randomness settings (which use deviation from and return to metronomic time in an effort to emulate human rhythm) and generate digital pulse based rhythm without hegemonic reliance on metronomic time. What kinds of information can be compiled or inferred from actual performed rhythm to make a rhythm machine whose fealty is not just to clock time?

Returning to the count-off, it seems unlikely that musicians could take an uttered “1…2…1..2..3..4!” without context and turn it into a groove. The count-off is more of a final declaration of tempo that begins with an acknowledgement of musicians present (“here we are, for whatever reasons, about to play music”), continues to a more specific statement of repertory (“let’s do “The Flintstone’s Theme Song”"), and ends with the count-off (“1…2…1..2..3..4!”). Along the way to the count-off there’s a whole lot of other contextual information that refines each musician’s sense of how to exhibit “good time” in the performed music. So the count-off, interesting as it is phenomenologically, is only a small part of the rhythmic information available to performers when starting a piece. An awareness of these musical and socio-cultural contexts may help to define a new “hyper-metronomic” approach to computerized rhythm.

The following example illustrates the disconnect between “good time” and metronomic time. Pianist Harold Mabern recorded “Seminole” on his 1991 album Straight Street with Ron Carter, bass, and Jack Dejohnette, drums. More than players with good time, this trio consists of genre-defining jazz musicians whose rhythmic concepts have been central to the development of jazz over the past 50 years. Straight Street was well received, and a Critics Pick for top ten jazz album in The Village Voice in 1992. A 64 measure AABA song form based on Ray Noble’s “Cherokee,” “Seminole” speeds up significantly from its original tempo. Here are the first two A sections:

Download audio file (mabern_seminole_top.mp3)

After a full chorus of melody and three choruses of piano solo the final chorus is reached. Here are the first two A sections of the final chorus:

Download audio file (mabern_seminole_out.mp3)

From a starting tempo where quarter notes are about 156 per minute the piece speeds up (especially in the first 96 measures) to about 210 quarter notes per minute, and by the second bridge is about 33% faster than at the beginning. While this speeding up is noticeable, and quite apparent at the final chorus, to my ears the music and players have “good time,” and I do feel invited into their musical space. Further, each member of the trio is an authoritative figure in the world of jazz. For some listeners this amount of acceleration could reveal bad time, an inability to maintain metronomic accuracy, and, if the trio were lesser known players I might join (right or wrong) in this judgement. Without a doubt if I were making an album and realized a piece accelerated that much I’d want to re-record it. It’s possible but unlikely that the trio intentionally sped up.

If great musicians speed up (and slow down) great drum machines should too! While the example above is a clear case of speeding up, most (all?) real performances of groove based music deviate in more subtle ways from metronomic time, both within metrical units (between beats and small groups of beats) and at the phrase or form level. Can this aspect of human musical timekeeping be integrated into digital rhythm programming? In the first part of this series I proposed that musicians in groove based music tend to “metronomize” or internalize a rhythmic pattern that’s syncopated in relation to the principal strong beats of their part. How are these internalized patterns influenced by the various contexts (both communally shared and individual) provided by the musical moment, and temporally stretched or compressed in reaction to these contexts?

Download audio file (mabern_seminole.mp3)

“Seminole” Harold Mabern, piano; Ron Carter, bass, Jack Dejohnette, drums.

Harold Mabern’s Amazon page

“Seminole” is a contrafact based on “Cherokee,” a very standard jazz standard. “Cherokee” is attributed to bandleader and composer Ray Noble (although he is known to have bought others’ compositions and put his name on them), and its song structure is notable in the development of bebop as the basis of Charlie Parker’s “Koko” and other jazz improvisational frameworks. “Cherokee” and its contrafacts are well known in jazz for their very quick tempi, often performed at speeds above 300 quarter notes per minute. “Cherokee’s” largely pentatonic melody has lots of whole and half notes, and the melody is usually played by horn players. Mabern’s “Seminole,” on the contrary, is played at a medium tempo and the A section melody is arranged for string bass and piano in unison. These contrasts between “Cherokee” and “Seminole” are contributing factors to the speeding up discernible in Mabern’s recording. I had first hand experience playing “Cherokee” with Harold Mabern at the NYC local 802 jazz jam. He called the tune in an exotic key (E major) and counted off a blistering tempo. Most jazz musicians have played “Cherokee” many times, and usually very quickly. These remembered experiences of playing “Cherokee” fast predispose the trio to push the tempo in their performance of “Seminole.”

The first two A sections of “Seminole” maintain a more or less metronomic tempo, though the passages with triplets do speed up a bit. Carter begins to play time rather than the melody from the last four measures of the second A into B, and this familiar texture initiates a more intense speeding up. The B section (:52- 1:12) is marked by a recurring 2-measure rhythmic displacement, “hits” marked by the whole group. Here the speeding up is apparent; especially at :55-1:01 Mabern’s chords anticipate Dejohnette’s brushes, and by the last A of the opening melody the piece has almost reached its quickest tempo. By the B section of the second chorus “Seminole” has a stable tempo of around 210 quarter notes per minute, which is more or less maintained until the end of the piece.

Chris Dobrian, one of the great professors I studied with at UC Irvine, once mentioned to me that when music speeds up or slows down in this way it’s often because the chosen tempo is not well suited to the composition. Was Mabern’s count-off too slow? It’s hard to say. The initial tempo is a great but difficult “in the pocket” type of medium swing. It seems more likely to me that the trio (especially Carter), perhaps under-rehearsed, was a bit tense and working hard to read through the first two A sections, and when the more open feel leading to B arrived the group (especially Mabern) was glad to release this tension as an increase in tempo. When the rhythmic hits at B arrived Mabern and Carter both ran with the feeling of release, and the performance sped up a lot. It’s notable that Dejohnette compensates for this shift almost instantaneously, maintaining the trio’s good time even as the tempo is really taking off.

This analysis is based not on any firsthand knowledge of the participating musicians’ recording process but is instead my own impression of the way in which the piece speeds up. I am projecting personal assumptions and tendencies onto the rhythmic phenomena found in “Seminole.” While surely inaccurate and more guesswork than fact-based inference, it is just these kinds of fuzzily logical processes that inform musical enactment of rhythm. For the organic drum machine to have good time, to emulate real human rhythmic inflection, it must also make educated guesses about where tempo and inflections should be, just as this top-level trio does in “Seminole,” and I do in analyzing “Seminole.”

Where should I start with creating these kinds of algorithms? When Ron McClure counted off “Alice in Wonderland” and I was not able to effectively take the count-off the problem was partly my novice abilities. Beyond that, however, I did not have the context necessary. Once we were able to play the piece I recalled that it was performed by Bill Evans on the classic album Sunday at the Village Vanguard. I didn’t know the song by its title, but, once I played it, I was able to access my own memory of Evans’ recording of the piece (a memory which I am sure McClure shared). Given that shared context a count-off becomes much less important. I have since played “Alice in Wonderland” dozens of times, and each version’s tempo has probably been within 20% of Evans’ recorded tempo. The organic drum machine should have just this kind of hyper-metronomic contextual information such that, for an extreme example, if you used it to play “Alice in Wonderland,” but indicated a tempo much slower than that of recorded versions of the piece, it might tend to speed up a bit. Further, such a drum machine should maintain a sense of form and exhibit some of the classic human behaviors; sometimes rushing into new sections, tending to pull back or join in when others seem to speed up. These are all rather advanced programming concepts, and, while they tilt at a phrase-level hyper-metronomic system for contextualizing performed rhythm, rely on a rational framework for defining beats and beat subdivision. Part three of this series will discuss what beats are and how to find them using max/msp/jitter further discuss phenomenon related to human execution of rhythm.