Combining Real-Time Extraction and Prediction of Musical Chord Progressions for Creative Applications

Recently, the field of musical co-creativity has gained some momentum. In this context, our goal is twofold: to develop an intelligent listening and predictive module of chord sequences, and to propose an adapted evaluation of the associated Music Information Retrieval (MIR) tasks that are the real-...

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Autores principales:	Tristan Carsault, Jérôme Nika, Philippe Esling, Gérard Assayag
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	informatics music chords learning co-creativity co-improvisation Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/21141a96168c4ada8599f768391dcb05
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spelling	oai:doaj.org-article:21141a96168c4ada8599f768391dcb052021-11-11T15:38:51ZCombining Real-Time Extraction and Prediction of Musical Chord Progressions for Creative Applications10.3390/electronics102126342079-9292https://doaj.org/article/21141a96168c4ada8599f768391dcb052021-10-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2634https://doaj.org/toc/2079-9292Recently, the field of musical co-creativity has gained some momentum. In this context, our goal is twofold: to develop an intelligent listening and predictive module of chord sequences, and to propose an adapted evaluation of the associated Music Information Retrieval (MIR) tasks that are the real-time extraction of musical chord labels from a live audio stream and the prediction of a possible continuation of the extracted symbolic sequence. Indeed, this application case invites us to raise questions about the evaluation processes and methodology that are currently applied to chord-based MIR models. In this paper, we focus on <i>musical chords</i> since these mid-level features are frequently used to describe harmonic progressions in Western music. In the case of chords, there exists some strong inherent hierarchical and functional relationships. However, most of the research in the field of MIR focuses mainly on the performance of chord-based statistical models, without considering music-based evaluation or learning. Indeed, usual evaluations are based on a binary qualification of the classification outputs (right chord predicted versus wrong chord predicted). Therefore, we present a specifically-tailored chord analyser to measure the performances of chord-based models in terms of functional qualification of the classification outputs (by taking into account the harmonic function of the chords). Then, in order to introduce musical knowledge into the learning process for the automatic chord extraction task, we propose a specific musical distance for comparing predicted and labeled chords. Finally, we conduct investigations into the impact of including high-level metadata in chord sequence prediction learning (such as information on key or downbeat position). We show that a model can obtain better performances in terms of accuracy or perplexity, but output biased results. At the same time, a model with a lower accuracy score can output errors with more musical meaning. Therefore, performing a goal-oriented evaluation allows a better understanding of the results and a more adapted design of MIR models.Tristan CarsaultJérôme NikaPhilippe EslingGérard AssayagMDPI AGarticleinformaticsmusicchordslearningco-creativityco-improvisationElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2634, p 2634 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	informatics music chords learning co-creativity co-improvisation Electronics TK7800-8360
spellingShingle	informatics music chords learning co-creativity co-improvisation Electronics TK7800-8360 Tristan Carsault Jérôme Nika Philippe Esling Gérard Assayag Combining Real-Time Extraction and Prediction of Musical Chord Progressions for Creative Applications
description	Recently, the field of musical co-creativity has gained some momentum. In this context, our goal is twofold: to develop an intelligent listening and predictive module of chord sequences, and to propose an adapted evaluation of the associated Music Information Retrieval (MIR) tasks that are the real-time extraction of musical chord labels from a live audio stream and the prediction of a possible continuation of the extracted symbolic sequence. Indeed, this application case invites us to raise questions about the evaluation processes and methodology that are currently applied to chord-based MIR models. In this paper, we focus on <i>musical chords</i> since these mid-level features are frequently used to describe harmonic progressions in Western music. In the case of chords, there exists some strong inherent hierarchical and functional relationships. However, most of the research in the field of MIR focuses mainly on the performance of chord-based statistical models, without considering music-based evaluation or learning. Indeed, usual evaluations are based on a binary qualification of the classification outputs (right chord predicted versus wrong chord predicted). Therefore, we present a specifically-tailored chord analyser to measure the performances of chord-based models in terms of functional qualification of the classification outputs (by taking into account the harmonic function of the chords). Then, in order to introduce musical knowledge into the learning process for the automatic chord extraction task, we propose a specific musical distance for comparing predicted and labeled chords. Finally, we conduct investigations into the impact of including high-level metadata in chord sequence prediction learning (such as information on key or downbeat position). We show that a model can obtain better performances in terms of accuracy or perplexity, but output biased results. At the same time, a model with a lower accuracy score can output errors with more musical meaning. Therefore, performing a goal-oriented evaluation allows a better understanding of the results and a more adapted design of MIR models.
format	article
author	Tristan Carsault Jérôme Nika Philippe Esling Gérard Assayag
author_facet	Tristan Carsault Jérôme Nika Philippe Esling Gérard Assayag
author_sort	Tristan Carsault
title	Combining Real-Time Extraction and Prediction of Musical Chord Progressions for Creative Applications
title_short	Combining Real-Time Extraction and Prediction of Musical Chord Progressions for Creative Applications
title_full	Combining Real-Time Extraction and Prediction of Musical Chord Progressions for Creative Applications
title_fullStr	Combining Real-Time Extraction and Prediction of Musical Chord Progressions for Creative Applications
title_full_unstemmed	Combining Real-Time Extraction and Prediction of Musical Chord Progressions for Creative Applications
title_sort	combining real-time extraction and prediction of musical chord progressions for creative applications
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/21141a96168c4ada8599f768391dcb05
work_keys_str_mv	AT tristancarsault combiningrealtimeextractionandpredictionofmusicalchordprogressionsforcreativeapplications AT jeromenika combiningrealtimeextractionandpredictionofmusicalchordprogressionsforcreativeapplications AT philippeesling combiningrealtimeextractionandpredictionofmusicalchordprogressionsforcreativeapplications AT gerardassayag combiningrealtimeextractionandpredictionofmusicalchordprogressionsforcreativeapplications
_version_	1718434654134992896

Combining Real-Time Extraction and Prediction of Musical Chord Progressions for Creative Applications

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