ANALYSIS OF COMMON DESIGN CHOICES IN DEEP LEARNING SYSTEMS FOR DOWNBEAT TRACKING - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2018

ANALYSIS OF COMMON DESIGN CHOICES IN DEEP LEARNING SYSTEMS FOR DOWNBEAT TRACKING

Résumé

Downbeat tracking consists of annotating a piece of musical audio with the estimated position of the first beat of each bar. In recent years, increasing attention has been paid to applying deep learning models to this task, and various architectures have been proposed, leading to a significant improvement in accuracy. However, there are few insights about the role of the various design choices and the delicate interactions between them. In this paper we offer a systematic investigation of the impact of largely adopted variants. We study the effects of the temporal granularity of the input representation (i.e. beat-level vs tatum-level) and the encoding of the networks outputs. We also investigate the potential of convolutional-recurrent networks, which have not been explored in previous downbeat tracking systems. To this end, we exploit a state-of-the-art recurrent neural network where we introduce those variants, while keeping the training data, network learning parameters and post-processing stages fixed. We find that temporal granularity has a significant impact on performance, and we analyze its interaction with the encoding of the networks outputs.
Fichier principal
Vignette du fichier
MF_ISMIR-18.pdf (867.99 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02943467 , version 1 (19-09-2020)

Identifiants

  • HAL Id : hal-02943467 , version 1

Citer

Magdalena Fuentes, Brian Mcfee, Hélène C Crayencour, Slim Essid, Juan P Bello. ANALYSIS OF COMMON DESIGN CHOICES IN DEEP LEARNING SYSTEMS FOR DOWNBEAT TRACKING. The 19th International Society for Music Information Retrieval Conference, Sep 2018, Paris, France. ⟨hal-02943467⟩
138 Consultations
128 Téléchargements

Partager

Gmail Facebook X LinkedIn More