Singing Style Investigation by Residual Siamese Convolutional Neural Networks
Cheng-i Wang, George Tzanetakis · 2018
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.