Singing Voice Detection Based on a Deeper Convolutional Neural Network

Dec 2022·
Wenming Gui
Zeyu Xia
Zeyu Xia
,
Rubin Gong
,
Gui Wang
,
Bingxu Chen
,
Donghui Zhang
Abstract
Singing voice detection is a fundamental task in music information retrieval, which benefits other tasks such as singing voice separation. We propose a new algorithm based on a deeper convolution neural network, fed with the logarithmic and mel-scaled spectrogram, to exact and integrate the features of the different layers of the network and to discriminate the singing voice finally. We demonstrate that this deeper network can produce good performances and be designed efficiently to some extent. The experiments are based on the public datasets: Jamendo, Mir1k, RWC pop, and their combined dataset. We also studied what depth of the network is suitable for this task. The experiments show that the optimal depth on the four public datasets is 152.
Type
Publication
Proceedings of the 3rd International Symposium on Automation, Information and Computing (ISAIC 2022), 1, 336-341. SCITEPRESS - Science and Technology Publications
publications
Singing Voice Detection Deeper Convolutional Neural Network Recurrent Neural Network Squeeze and Excitation Residual Convolutional Network
Zeyu Xia
Authors
Zeyu Xia (he/him)
Computer Science PhD Student

I am a Computer Science PhD student at the University of Virginia, advised by Prof. Geoffrey C. Fox. My research focuses on generative models and differentiable simulation for science. I build efficient methods for scientific data generation and inverse problems, and evaluate how well models capture physical behavior.

I also contribute to DeepVELA, exploring agentic benchmarks for scientific applications in collaboration with MLCommons.