Commentary on “An Information-Theoretical Method for Comparing Completions of Contrapunctus XIV from Bach’s Art of Fugue”

Information entropy can be a powerful tool for analyzing differences in musical style as suggested in Paz et al. (2022). The application of such a tool, however, requires unambiguous operationalization of the variables being measured, and establishment of clear relations between the variables and the distinguishing features of the style. Without these, interpreting entropy, or any other relative measure of difference, leads to no useful conclusions regarding the similarity between style

Some Aspects of Pedagogical Corpora

This essay focuses on the characteristics of corpora drawn from pedagogical materials and contrasts them with the properties of corpora of larger repertoires. Two case studies show pedagogical corpora to contain relatively more chromaticism, and to devote more of their probability mass to low-frequency events. This is likely due to the formatting of and motivation behind classroom materials (for example, focusing proportionately more resources on difficult concepts). I argue that my observations challenge the utility of using pedagogical corpora within research into implicit learning. I also suggest that these datasets are uniquely situated to yield insights into explicit learning, and into how musical traditions are represented in the classroom

Mozart’s music between predictability and surprise: results of an experimental research based on electroencephalography, entropy and Hurst exponent

OBJECTIVE: The main goal of our work was to simultaneously study musical and electroencephalogram (EEG) signal while listening to Mozart’s K448 Sonata, a piece known for the “Mozart effect”, with the aim to better understand the reasons of beneficial effect of music on the brain. DESIGN: To this purpose, in a small sample of young healthy subjects, we examined the EEG correlates of modifications of brain activity, also applying the concepts of entropy and Hurst exponent H to K448 Sonata compared to a selection of Mozart’s excerpts, so that to expose the peculiar characteristics of this compositions in terms of predictability and surprise for the listener RESULTS: Spectral analysis showed that mean beta rhythm significantly grew during the listening to K448, and that this effect remaining immediately after, but to a lesser extent. Furthermore, we found that maximum values of entropy and lower values of H were reached by K448 compared to a selection of Mozart’s pieces. CONCLUSIONS: The results support the hypothesis of an overall effect of activation of the superior cortical functions during listening to K448, and immediately afterwards, in healthy young adults, and of a greater complexity of this sonata compared to a selection of Mozart’s pieces

Generation of folk song melodies using Bayes transforms

The paper introduces the `Bayes transform', a mathematical procedure for putting data into a hierarchical representation. Applicable to any type of data, the procedure yields interesting results when applied to sequences. In this case, the representation obtained implicitly models the repetition hierarchy of the source. There are then natural applications to music. Derivation of Bayes transforms can be the means of determining the repetition hierarchy of note sequences (melodies) in an empirical and domain-general way. The paper investigates application of this approach to Folk Song, examining the results that can be obtained by treating such transforms as generative models

Neural Entrainment is Associated with Subjective Groove and Complexity for Performed but not Mechanical Musical Rhythms

Both movement and neural activity in humans can be entrained by the regularities of an external stimulus, such as the beat of musical rhythms. Neural entrainment to auditory rhythms supports temporal perception, and is enhanced by selective attention and by hierarchical temporal structure imposed on rhythms. However, it is not known how neural entrainment to rhythms is related to the subjective experience of groove (the desire to move along with music or rhythm), the perception of a regular beat, the perception of complexity, and the experience of pleasure. In two experiments, we used musical rhythms (from Steve Reich’s Clapping Music) to investigate whether rhythms that are performed by humans (with naturally variable timing) and rhythms that are mechanical (with precise timing), elicit differences in 1) neural entrainment, as measured by inter-trial phase coherence, and 2) subjective ratings of the complexity, preference, groove, and beat strength of rhythms. We also combined results from the two experiments to investigate relationships between neural entrainment and subjective perception of musical rhythms. We found that mechanical rhythms elicited a greater degree of neural entrainment than performed rhythms, likely due to the greater temporal precision in the stimulus, and the two types only elicited different ratings for some individual rhythms. Neural entrainment to performed rhythms, but not to mechanical ones, correlated with subjective desire to move and subjective complexity. These data therefore suggest multiple interacting influences on neural entrainment to rhythms, from low-level stimulus properties to high-level cognition and perception

Methodological and musicological investigation of the System & Contrast model for musical form description

The semiotic description of music structure aims at representing the high-level organization of music pieces in a concise, generic and reproducible way as a low-rate stream of arbitrary symbols from a limited alphabet, which results into a sequence of " semiotic units ". In this context, the purpose of the System & Contrast model is to address the internal organization of the semiotic units. In this report, the System & Contrast model is approached from different angles in relation to varied disciplines : cognitive psychology, music analysis and information theory. After establishing a number of links between the System & Contrast model and other approaches of music structure, the model is illustrated on studio-based popular music pieces, as well as on music from the classical Viennese period

Culturally sensitive strategies for automatic music prediction

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 103-112).Music has been shown to form an essential part of the human experience-every known society engages in music. However, as universal as it may be, music has evolved into a variety of genres, peculiar to particular cultures. In fact people acquire musical skill, understanding, and appreciation specific to the music they have been exposed to. This process of enculturation builds mental structures that form the cognitive basis for musical expectation. In this thesis I argue that in order for machines to perform musical tasks like humans do, in particular to predict music, they need to be subjected to a similar enculturation process by design. This work is grounded in an information theoretic framework that takes cultural context into account. I introduce a measure of musical entropy to analyze the predictability of musical events as a function of prior musical exposure. Then I discuss computational models for music representation that are informed by genre-specific containers for musical elements like notes. Finally I propose a software framework for automatic music prediction. The system extracts a lexicon of melodic, or timbral, and rhythmic primitives from audio, and generates a hierarchical grammar to represent the structure of a particular musical form. To improve prediction accuracy, context can be switched with cultural plug-ins that are designed for specific musical instruments and genres. In listening experiments involving music synthesis a culture-specific design fares significantly better than a culture-agnostic one. Hence my findings support the importance of computational enculturation for automatic music prediction. Furthermore I suggest that in order to sustain and cultivate the diversity of musical traditions around the world it is indispensable that we design culturally sensitive music technology.by Mihir Sarkar.Ph.D

The Computational Attitude in Music Theory

Music studies’s turn to computation during the twentieth century has engendered particular habits of thought about music, habits that remain in operation long after the music scholar has stepped away from the computer. The computational attitude is a way of thinking about music that is learned at the computer but can be applied away from it. It may be manifest in actual computer use, or in invocations of computationalism, a theory of mind whose influence on twentieth-century music theory is palpable. It may also be manifest in more informal discussions about music, which make liberal use of computational metaphors. In Chapter 1, I describe this attitude, the stakes for considering the computer as one of its instruments, and the kinds of historical sources and methodologies we might draw on to chart its ascendance. The remainder of this dissertation considers distinct and varied cases from the mid-twentieth century in which computers or computationalist musical ideas were used to pursue new musical objects, to quantify and classify musical scores as data, and to instantiate a generally music-structuralist mode of analysis. I present an account of the decades-long effort to prepare an exhaustive and accurate catalog of the all-interval twelve-tone series (Chapter 2). This problem was first posed in the 1920s but was not solved until 1959, when the composer Hanns Jelinek collaborated with the computer engineer Heinz Zemanek to jointly develop and run a computer program. Recognizing the transformation wrought on modern statistics and communications technology by information theory, I revisit Abraham Moles’s book Information Theory and Esthetic Perception (orig. 1958) and use its vocabulary to contextualize contemporary information-theoretic work on music that various evokes the computational mind by John. R. Pierce and Mary Shannon, Wilhelm Fucks, and Henry Quastler (Chapter 3). I conclude with a detailed look into a score-segmentation algorithm of the influential American music theorist Allen Forte (Chapter 4). Forte was a skilled programmer who spent several years at MIT in the 1960s, with cutting-edge computers and the company of first-rank figures in the nascent fields of computer science and artificial intelligence. Each one of the researchers whose work is treated in these case studies—at some stage in their relationship with music—adopted what I call the computational attitude to music, to varying degrees and for diverse ends. Of the many questions this dissertation seeks to answer: what was gained by adopting such an attitude? What was lost? Having understood these past explorations of the computational attitude to music, we are better suited ask of ourselves the same questions today