Titre :

Not All Roads Lead to Rome: Pitch Representation and Model Architecture for Automatic Harmonic Analysis

Auteur(s) :

Micchi, Gianluca [Auteur]
Algomus
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Gotham, Mark [Auteur]
Cornell University [New York]
Giraud, Mathieu [Auteur]
Algomus
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Titre de la revue :

Transactions of the International Society for Music Information Retrieval (TISMIR)

Pagination :

42-54

Éditeur :

Ubiquity Press

Date de publication :

2020

ISSN :

2514-3298

Mot(s)-clé(s) en anglais :

tonality
Roman numeral analysis
functional harmony
machine learning
pitch encoding
corpus
computer music

Discipline(s) HAL :

Sciences de l'Homme et Société/Musique, musicologie et arts de la scène
Informatique [cs]/Son [cs.SD]
Informatique [cs]/Apprentissage [cs.LG]

Résumé en anglais : [en]

Automatic harmonic analysis has been an enduring focus of the MIR community, and has enjoyed a particularly vigorous revival of interest in the machine-learning age. We focus here on the specific case of Roman numeral ...
Lire la suite >Automatic harmonic analysis has been an enduring focus of the MIR community, and has enjoyed a particularly vigorous revival of interest in the machine-learning age. We focus here on the specific case of Roman numeral analysis which, by virtue of requiring key/functional information in addition to chords, may be viewed as an acutely challenging use case.We report on three main developments. First, we provide a new meta-corpus bringing together all existing Roman numeral analysis datasets; this offers greater scale and diversity, not only of the music represented, but also of human analytical viewpoints. Second, we examine best practices in the encoding of pitch, time, and harmony for machine learning tasks. The main contribution here is the introduction of full pitch spelling to such a system, an absolute must for the comprehensive study of musical harmony. Third, we devised and tested several neural network architectures and compared their relative accuracy. In the best-performing of these models, convolutional layers gather the local information needed to analyse the chord at a given moment while a recurrent part learns longer-range harmonic progressions.Altogether, our best representation and architecture produce a small but significant improvement on overall accuracy while simultaneously integrating full pitch spelling. This enables the system to retain important information from the musical sources and provide more meaningful predictions for any new input.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL