Herbie Hancock's Chameleon's BPM graph from the Android app 'liveBPM' (v. 1.2.0) by Daniel Bach

Listening to music seems easy.

Listening to music seems easy; it even appears like a passive task.

Listening, however, is not the same as hearing. In listening, i.e., attending, we add cognition to perception. The cognition of musical structures, cultural meanings, conventions, and even of the most fundamental elements themselves such as pitch or rhythm turns out to be a complex cognitive task. We know this is so because getting our cutting-edge technology to understand music with all its subtleties and its cultural contexts has proven, so far, to be impossible.

Within small fractions of a second, humans can reach conclusions about musical audio that are beyond the abilities of the most advanced algorithms.

For example, a trained or experienced musician (or even non-musician listener) can differentiate computer-generated and human-performed instruments in almost any musical input, even in the presence of dozens of other instruments sounding simultaneously.

In a rather different case, humans can maintain time-organizational internal representations of music while the tempo of a recording or performance continuously changes. A classic example is the jazz standard Chameleon by Herbie Hancock off the album ‘HEADHUNTERS’. The recording never retains any one tempo, following an up-and-down contour and mostly getting faster. Because tempo recognition is a prerequisite to other music-perception tasks like meter induction and onset detection, this type of behavior presents a significant challenge to signal-processing and machine-learning algorithms but generally poses no difficulty to human perception.

Another example is the recognition of vastly different cover versions of songs: A person familiar with a song can recognize within a few notes a cover version of that song done in another genre, at a different tempo, by another singer, and with different instrumentation.

Each of these is a task that is well beyond machine-learning techniques that are exhibiting remarkable successes with visual recognition where the main challenge, invariance, is less of an obstacle than the abstractness of music and its seemingly arbitrary meanings and structures.

Consider the following aspects of music cognition.

  • inferring a key (or a change of key) from very few notes
  • identifying a latent underlying pulse when it is completely obscured by syncopation [Tal et al., Missing Pulse]
  • effortlessly tracking key changes, tempo changes, and meter changes
  • instantly separating and identifying instruments even in performances with many-voice polyphony (as in Dixieland Jazz, Big-Band Jazz, Baroque and Classical European court music, Progressive Rock, folkloric Rumba, and Hindustani and Carnatic classical music)

These and many other forms of highly polyphonic, polyrhythmic, or cross-rhythmic music continue to present challenges to automated algorithms. Successful examples of automated tempo or meter induction, onset detection, source separation, key detection, and the like all work under the requirement of tight limitations on the types of inputs. Even for a single such task such as source separation, a universally applicable algorithm does not seem to exist. (There is some commercial software that appear to do these tasks universally, but because proprietary programs do not provide sufficiently detailed outputs, whether they really can perform all these function or whether they perform one function in enough detail to suffice for studio uses is uncertain. One such suite can identify and separate every individual note from any recording, but does not perform source separation into streams-per-instrument and presents its output in a form not conducive to analysis in rhythmic, harmonic, melodic, or formal terms, and not in a form analogous to human cognitive processing of music.)

Not only does universal music analysis remain an unsolved problem, but also most of the world’s technological effort goes toward European folk music, European classical music, and (international) popular music. The goal of my research and my lab (Lab BBBB: Beats, Beats, Bayes, and the Brain) is to develop systems for culturally sensitive and culturally informed music analysis, music coaching, automated accompaniment, music recommendation, and algorithmic composition, and to do so for popular music styles from the Global South that are not in the industry’s radar.

Since the human nervous system is able to complete musical-analysis tasks under almost any set of circumstances, in multiple cultural and cross-cultural settings, with varying levels of noise and interference, the human brain is still superior to the highest-level technology we have developed. Hence, Lab BBBB takes inspiration and direct insight from human neural processing of audio and music to solve culturally specific cognitive problems in music analysis, and to use this context to further our understanding of neuroscience and machine learning.

The long-term goal of our research effort is a feedback cycle:

  1. Neuroscience (in simulation and with human subjects at our collaborators’ sites) informs both music information retrieval and research into neural-network structures (machine learning). We are initially doing this by investigating the role of rhythm priming in Parkinson’s (rhythm–motor interaction) and in grammar-learning performance (rhythm–language interaction) in the basal ganglia. We hope to then replicate in simulation the effects that have been observed with people, verify our models, and use our modeling experience on other tasks that have not yet been demonstrated in human cases or that are too invasive or otherwise unacceptable.
  2. Work on machine learning informs neuroscience by narrowing down the range of investigation.
  3. Deep learning is also used to analyze musical audio using structures closer to those in the human brain than the filter-bank and matrix-decomposition methods typically used to analyze music.
  4. Music analysis informs cognitive neuroscience, we conjecture, as have been done in certain cases in the literature with nonlinear dynamics.
  5. Phenomena like entrainment and neural resonance in neurodynamics further inform the development of neural-network structures and data-subspace methods.
  6. These developments in machine learning move music information retrieval closer to human-like performance for culturally informed music analysis, music coaching, automated accompaniment, music recommendation, and algorithmic composition for multicultural intelligent music systems.



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