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Monoaural Audio Source Separation Using Deep Convolutional Neural Networks

2017; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-319-53547-0_25

1611-3349

Pritish Chandna, Marius Miron, Jordi Janer, Emília Gómez,

Blind Source Separation Techniques

In this paper we introduce a low-latency monaural source separation framework using a Convolutional Neural Network (CNN). We use a CNN to estimate time-frequency soft masks which are applied for source separation. We evaluate the performance of the neural network on a database comprising of musical mixtures of three instruments: voice, drums, bass as well as other instruments which vary from song to song. The proposed architecture is compared to a Multilayer Perceptron (MLP), achieving on-par results and a significant improvement in processing time. The algorithm was submitted to source separation evaluation campaigns to test efficiency, and achieved competitive results.