Evolution of Music by Public Choice

Music evolves as composers, performers, and consumers favor some musical variants over others. To investigate the role of consumer selection, we constructed a Darwinian music engine consisting of a population of short audio loops that sexually reproduce and mutate. This population evolved for 2,513 generations under the selective influence of 6,931 consumers who rated the loops’ aesthetic qualities. We found that the loops quickly evolved into music attributable, in part, to the evolution of aesthetically pleasing chords and rhythms. Later, however, evolution slowed. Applying the Price equation, a general description of evolutionary processes, we found that this stasis was mostly attributable to a decrease in the fidelity of transmission. Our experiment shows how cultural dynamics can be explained in terms of competing evolutionary forces

Loop-aware Audio Recording for the Web

Music loops are audio recordings used as basic building blocks in many types of music. The use of pre-recorded loops facilitates engagement into music creation to users regardless of their background in music theory. Using online loop databases also affords simple collaboration and exchange. Hence, music loops are particularly attractive for web audio applications. However, traditional musical audio recording typically relies on complex DAW software. Recording loops usually requires consideration of musical meter and tempo, and withstanding metronome sounds. In this paper, we propose loop-aware audio recording as a use case for web audio technologies. Our approach supports hands-free, low-stress recording of music loops in web- enabled devices. The system is able to detect repetitions in an incoming audio stream. Based on this information, it segments and ranks the repeated fragments, presenting the list to the user. We provide an example implementation, and evaluate the use of the different MIR libraries available in the web audio platform for the proposed task

Automatic characterization and generation of music loops and instrument samples for electronic music production

Repurposing audio material to create new music - also known as sampling - was a foundation of electronic music and is a fundamental component of this practice. Currently, large-scale databases of audio offer vast collections of audio material for users to work with. The navigation on these databases is heavily focused on hierarchical tree directories. Consequently, sound retrieval is tiresome and often identified as an undesired interruption in the creative process. We address two fundamental methods for navigating sounds: characterization and generation. Characterizing loops and one-shots in terms of instruments or instrumentation allows for organizing unstructured collections and a faster retrieval for music-making. The generation of loops and one-shot sounds enables the creation of new sounds not present in an audio collection through interpolation or modification of the existing material. To achieve this, we employ deep-learning-based data-driven methodologies for classification and generation.Repurposing audio material to create new music - also known as sampling - was a foundation of electronic music and is a fundamental component of this practice. Currently, large-scale databases of audio offer vast collections of audio material for users to work with. The navigation on these databases is heavily focused on hierarchical tree directories. Consequently, sound retrieval is tiresome and often identified as an undesired interruption in the creative process. We address two fundamental methods for navigating sounds: characterization and generation. Characterizing loops and one-shots in terms of instruments or instrumentation allows for organizing unstructured collections and a faster retrieval for music-making. The generation of loops and one-shot sounds enables the creation of new sounds not present in an audio collection through interpolation or modification of the existing material. To achieve this, we employ deep-learning-based data-driven methodologies for classification and generation

Syncopation and the Score

The first and second authors were supported by an EPSRC DTA (www.epsrc.ac.uk) studentship

A Comprehensive Trainable Error Model for Sung Music Queries

We propose a model for errors in sung queries, a variant of the hidden Markov model (HMM). This is a solution to the problem of identifying the degree of similarity between a (typically error-laden) sung query and a potential target in a database of musical works, an important problem in the field of music information retrieval. Similarity metrics are a critical component of query-by-humming (QBH) applications which search audio and multimedia databases for strong matches to oral queries. Our model comprehensively expresses the types of error or variation between target and query: cumulative and non-cumulative local errors, transposition, tempo and tempo changes, insertions, deletions and modulation. The model is not only expressive, but automatically trainable, or able to learn and generalize from query examples. We present results of simulations, designed to assess the discriminatory potential of the model, and tests with real sung queries, to demonstrate relevance to real-world applications

Generative rhythmic models

A system for generative rhythmic modeling is presented. The work aims to explore computational models of creativity, realizing them in a system designed for realtime generation of semi-improvisational music. This is envisioned as an attempt to develop musical intelligence in the context of structured improvisation, and by doing so to enable and encourage new forms of musical control and performance; the systems described in this work, already capable of realtime creation, have been designed with the explicit intention of embedding them in a variety of performance-based systems. A model of qaida, a solo tabla form, is presented, along with the results of an online survey comparing it to a professional tabla player's recording on dimensions of musicality, creativity, and novelty. The qaida model generates a bank of rhythmic variations by reordering subphrases. Selections from this bank are sequenced using a feature-based approach. An experimental extension into modeling layer- and loop-based forms of electronic music is presented, in which the initial modeling approach is generalized. Starting from a seed track, the layer-based model utilizes audio analysis techniques such as blind source separation and onset-based segmentation to generate layers which are shuffled and recombined to generate novel music in a manner analogous to the qaida model.M.S.Committee Chair: Chordia, Parag; Committee Member: Freeman, Jason; Committee Member: Weinberg, Gi

Brain networks for temporal adaptation, anticipation, and sensory-motor integration in rhythmic human behavior

Human interaction often requires the precise yet flexible interpersonal coordination of rhythmic behavior, as in group music making. The present fMRI study investigates the functional brain networks that may facilitate such behavior by enabling temporal adaptation (error correction), prediction, and the monitoring and integration of information about ‘self’ and the external environment. Participants were required to synchronize finger taps with computer-controlled auditory sequences that were presented either at a globally steady tempo with local adaptations to the participants' tap timing (Virtual Partner task) or with gradual tempo accelerations and decelerations but without adaptation (Tempo Change task). Connectome-based predictive modelling was used to examine patterns of brain functional connectivity related to individual differences in behavioral performance and parameter estimates from the adaptation and anticipation model (ADAM) of sensorimotor synchronization for these two tasks under conditions of varying cognitive load. Results revealed distinct but overlapping brain networks associated with ADAM-derived estimates of temporal adaptation, anticipation, and the integration of self-controlled and externally controlled processes across task conditions. The partial overlap between ADAM networks suggests common hub regions that modulate functional connectivity within and between the brain's resting-state networks and additional sensory-motor regions and subcortical structures in a manner reflecting coordination skill. Such network reconfiguration might facilitate sensorimotor synchronization by enabling shifts in focus on internal and external information, and, in social contexts requiring interpersonal coordination, variations in the degree of simultaneous integration and segregation of these information sources in internal models that support self, other, and joint action planning and prediction

Understanding Anticipatory Time Perception in Consumers’ Time-Related Decisions

Anticipatory time (e.g., prospective duration into the future) is one of the key pieces of information to be processed in intertemporal decisions - decisions requiring a tradeoff between smaller sooner and larger delayed outcomes. Extensive research has examined human and animal perception of time as it is currently passing (i.e., experienced time) and time that has already passed (i.e., retrospective time). However, the nature of anticipatory time perception and its role in consumers’ judgment and decision making have been largely neglected. In my dissertation, I aim to demonstrate that considering subjective anticipatory time estimates offers a new perspective to understand intertemporal decisions. For this purpose, first, I propose that both diminishing sensitivity to longer time horizons (i.e., how long individuals perceive short time horizons to be relative to long time horizons) and the level of time contraction overall (i.e., how long or short individuals perceive time horizons to be overall) contribute to how much individuals discount the value of delayed outcomes, and, then, examine factors influencing intertemporal decisions by changing subjective time perception. Specifically, in the first and third essays, I demonstrate that sexually arousing images and auditory tempo (which has been shown to influence judgment of elapsed time) influence anticipatory time perception and subsequent intertemporal preferences. These results indicate that anticipatory time perception shares the property of perceptual inputs (e.g., people process anticipatory time as if they “perceive” elapsed time). In the second and fourth essays, I demonstrate that cognitive information available at the time of judging anticipatory time such as spatial distance and perceived life span influence individuals’ intertemporal preferences by changing their subjective perception of anticipatory time, which suggests that anticipatory time perception also has the property of embodied cognitions. Taken together, my dissertation incorporate both time perception research and consumer research on time-related judgment and decision making and sheds light on both domains