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A User’s Guide

The performance of music

Throughout the common-practice period (1650-1900), it is customary to find changes of musical key within a piece of music. In current music theory terminology, the concepts of modulation and tonicization are helpful to explain many of these changes of key. Conversely, in computational musicology and music information retrieval, the preferred way to denote changes of key are local key features, which are oftentimes predicted by computational models. Therefore, the three concepts, local keys, modulations, and toniciza-tions describe changes of key. What is, however, the relationship between the local keys, modulations, and tonicizations of the same musical fragment? In this paper, we contribute to this research question by 1) reviewing the current methods of local-key estimation, 2) providing a new dataset with annotated modulations and tonicizations, and 3) applying all the annotations (i.e., local keys, modulations, and tonicizations) in an experiment that connects the three concepts together. In our experiment, instead of assuming the music-theoretical meaning of the local keys predicted by an algorithm, we evaluate whether these coincide better with the modulation or tonicization annotations of the same musical fragment. Three existing models of symbolic local-key estimation, together with the annotated modulations and tonicizations of five music theory textbooks are considered during our evaluation. We provide the methodology of our experiment and our dataset (available at https://github.com/DDMAL/key_modulation_dataset) to motivate future research in the relationship between local keys, modulations, and tonicizations. * Both authors contributed equally to this work. • Applied computing → Sound and music computing; Fine arts; • Information systems → Retrieval tasks and goals; Evaluation of retrieval results.

https://hal.archives-ouvertes.fr/hal-02934937/document

On Local Keys, Modulations, and Tonicizations

Musicians perform music according to their own interpretations. How does a player realize his/her interpretation of a piece of music in the form of tones? How is his/her intention understood by a listener? The relations among the performers' intentions, intensity of tones produced by the performers, and listeners' perception were investigated quantitatively. The noteworthy findings were as follows. (1) Crescendo was easier to recognize than decrescendo. (2) It appeared that rising pitch enhanced an impression of crescendo, and falling pitch enhanced that of decrescendo. (3) The influence of “impression of the end” on crescendo was suggested. These findings were confirmed in the experiments by means of synthesized sound sequences and under well‐controlled conditions.

The communication of dynamics between musicians and listeners through musical performance

Previous works on chord recognition mainly focus on chord symbols but overlook other essential features that matter in musical harmony. To tackle the functional harmony recognition problem, we compile a new professionally annotated dataset of symbolic music encompassing not only chord symbols, but also various interrelated chord functions such as key modulation, chord inversion, secondary chords, and chord quality. We further present a novel holistic system in functional harmony recognition; a multi-task learning (MTL) architecture is implemented with the recurrent neural network (RNN) to jointly model chord functions in an end-to-end scenario. Experimental results highlight the capability of the proposed recognition system, and a promising improvement of the system by employing multi-task learning instead of single-task learning. This is one attempt to challenge the end-to-end chord recognition task from the perspective of functional harmony so as to uncover the grand structure ruling the flow of musical sound. The dataset and the source code of the proposed system is announced at https://github.com/ Tsung-Ping/functional-harmony.

/pdf/functional-harmony-recognition-of-symbolic-music-data-with-47fsh6fuja.pdf

Functional Harmony Recognition of Symbolic Music Data with Multi-task Recurrent Neural Networks

The Theme And Variation Encodings with Roman Numerals (TAVERN) dataset consists of 27 complete sets of theme and variations for piano composed between 1765 and 1810 by Mozart and Beethoven. In these theme and variation sets, comparable harmonic structures are realized in different ways. This facilitates an evaluation of the effectiveness of automatic analysis algorithms in generalizing across different musical textures. The pieces are encoded in standard **kern format, with analyses jointly encoded using an extension to **kern. The harmonic content of the music was analyzed with both Roman numerals and function labels in duplicate by two different expert analyzers. The pieces are divided into musical phrases, allowing for multiple-levels of automatic analysis, including chord labeling and phrase parsing. This paper describes the content of the dataset in detail, including the types of chords represented, and discusses the ways in which the analyzers sometimes disagreed on the lower-level harmonic content (the Roman numerals) while converging at similar high-level structures (the function of the chords within the phrase).

/pdf/theme-and-variation-encodings-with-roman-numerals-tavern-a-4njwu8mpw5.pdf

Theme And Variation Encodings with Roman Numerals (TAVERN): A New Data Set for Symbolic Music Analysis.

Music Information Retrieval (MIR) tends to focus on the analysis of audio signals. Often, a single music recording is used as representative of a "song" even though different performances of the same song may reveal different properties. A performance is distinct in many ways from a (arguably more abstract) representation of a "song," "piece," or musical score. The characteristics of the (recorded) performance -- as opposed to the score or musical idea -- can have a major impact on how a listener perceives music. The analysis of music performance, however, has been traditionally only a peripheral topic for the MIR research community. This paper surveys the field of Music Performance Analysis (MPA) from various perspectives, discusses its significance to the field of MIR, and points out opportunities for future research in this field.

Music Performance Analysis: A Survey

A perceptual study investigated the ability of scale degrees to evoke qualia, and the impact of harmonic context in shaping a scale degree’s qualia. In addition, the following questions were addressed: What role does music training have in shaping qualia? Are listeners consistent in their descriptions? Are experiences similar across participants, or are they individual and subjective? Listeners with or without music-theoretic training were asked to rate the qualia of scale degrees following various chord progressions, each ending with a different final harmony. Scale degrees were found to exhibit relatively consistent musical qualia; however, the local chord context was found to significantly influence qualia ratings. In general, both groups of listeners were found to be fairly consistent in their ratings of scale-degree qualia; however, as expected, musician listeners were more consistent than nonmusician listeners. Finally, a subset of the musical qualia ratings were compared against Krumhansl and Kessler’s (1982) scale-degree “profiles.” While profiles created from the present data, overall, were correlated with the K&K profiles, their claim that tonal stability accounts for the high ratings ascribed to tonic triad members was found to be better explained by the effect of the local chord context.

A Perceptual Study of Scale-degree Qualia in Context

A musical performance renders an acoustic realization of a musical score or other representation of a composition. Different performances of the same composition may vary in terms of performance parameters such as timing or dynamics, and these variations may have a major impact on how a listener perceives the music. The analysis of music performance has traditionally been a peripheral topic for the MIR research community, where often a single audio recording is used as representative of a musical work. This paper surveys the field of Music Performance Analysis (MPA) from several perspectives including the measurement of performance parameters, the relation of those parameters to the actions and intentions of a performer or perceptual effects on a listener, and finally the assessment of musical performance. This paper also discusses MPA as it relates to MIR, pointing out opportunities for collaboration and future research in both areas.

/pdf/an-interdisciplinary-review-of-music-performance-analysis-4pyavu4ho5.pdf

Claire Arthur

Papers

Theme And Variation Encodings with Roman Numerals (TAVERN): A New Data Set for Symbolic Music Analysis.

Music Performance Analysis: A Survey

Music Performance Analysis: A Survey

A Perceptual Study of Scale-degree Qualia in Context

An Interdisciplinary Review of Music Performance Analysis