• shareshare
  • link
  • cite
  • add
Powered by OpenAIRE graph
Found an issue? Give us feedback
auto_awesome_motion View all 7 versions
Publication . Article . 2021

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

Tristan Carsault; Jérôme Nika; Philippe Esling; Gérard Assayag;
Open Access
Published: 28 Oct 2021 Journal: Electronics, volume 10, issue 2,634 (issn: 2079-9292, Copyright policy )
Publisher: MDPI AG
Country: France

International audience; 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 musical chords 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.

Subjects by Vocabulary

ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION InformationSystems_INFORMATIONINTERFACESANDPRESENTATION(e.g.,HCI)

Microsoft Academic Graph classification: Perplexity Natural language processing computer.software_genre computer Statistical model Music information retrieval Computer science Field (computer science) Context (language use) Artificial intelligence business.industry business Chord (music) Key (music) Harmonic progression


informatics, music, chords, learning, co-creativity, co-improvisation, Electronics, TK7800-8360, signal, Electrical and Electronic Engineering, Computer Networks and Communications, Hardware and Architecture, Signal Processing, Control and Systems Engineering, [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [SHS.MUSIQ]Humanities and Social Sciences/Musicology and performing arts, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]

Related Organizations
Powered by OpenAIRE graph
Found an issue? Give us feedback
Funded by
Mixed Musical Reality with Creative Instruments
  • Funder: French National Research Agency (ANR) (ANR)
  • Project Code: ANR-19-CE33-0010
Raising co-creativity in cyber-human musicianship
  • Funder: European Commission (EC)
  • Project Code: 883313
  • Funding stream: H2020 | ERC | ERC-ADG
Validated by funder
Related to Research communities
Digital Humanities and Cultural Heritage