Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Music Tempo Estimation via Neural Networks -A Comparative Analysis

Abstract : This paper presents a comparative analysis on two artificial neural networks (with different architectures) for the task of tempo estimation. For this purpose, it also proposes the modeling, training and evaluation of a B-RNN (Bidirectional Recurrent Neural Network) model capable of estimating tempo in bpm (beats per minutes) of musical pieces, without using external auxiliary modules. An extensive database (12,550 pieces in total) was curated to conduct a quantitative and qualitative analysis over the experiment. Percussion-only tracks were also included in the dataset. The performance of the B-RNN is compared to that of state-of-the-art models. For further comparison, a state-of-the-art CNN was also retrained with the same datasets used for the B-RNN training. Evaluation results for each model and datasets are presented and discussed, as well as observations and ideas for future research. Tempo estimation was more accurate for the percussiononly dataset, suggesting that the estimation can be more accurate for percussion-only tracks, although further experiments (with more of such datasets) should be made to gather stronger evidence.
Complete list of metadata

https://hal.sorbonne-universite.fr/hal-03296892
Contributor : Jean-Pierre Briot <>
Submitted on : Thursday, July 22, 2021 - 9:18:20 PM
Last modification on : Wednesday, July 28, 2021 - 4:04:09 AM

File

2107.09208-arxiv.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03296892, version 1
  • ARXIV : 2107.09208

Citation

Mila Soares de Oliveira de Souza, Pedro Nuno de Souza Moura, Jean-Pierre Briot. Music Tempo Estimation via Neural Networks -A Comparative Analysis. 2021. ⟨hal-03296892⟩

Share

Metrics

Record views

13

Files downloads

12