Singing Style Investigation by Residual Siamese Convolutional Neural Networks

Cheng-i Wang; George Tzanetakis

ICASSP 2018accepted0 citations

Singing Style Investigation by Residual Siamese Convolutional Neural Networks

Abstract

Investigating singing style is a difficult problem as individual styles are intertwined with melodies from different songs. In this paper, a methodology to investigate singing style is proposed. The proposed approach utilizes convolutional neural networks in a siamese architecture. In addition, we investigate variants of the networks to improve the audio feature extraction process. The potential of the proposed method for analyzing singing style is demonstrated using experiments on pop music singing recordings. The results indicate that the use of the proposed method is indeed effective in learning audio features that are relevant for characterizing singing style.

BibTeX

@inproceedings{icassp2018_singingstyleinve,
  title = {Singing Style Investigation by Residual Siamese Convolutional Neural Networks},
  author = {Cheng-i Wang and George Tzanetakis},
  booktitle = {ICASSP 2018},
  year = {2018}
}