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arXiv · 1709.07908

Neural Network Alternatives to Convolutive Audio Models for Source Separation

Shrikant Venkataramani·Y. Cem Subakan·Paris Smaragdis
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Abstract

Convolutive Non-Negative Matrix Factorization model factorizes a given audio spectrogram using frequency templates with a temporal dimension. In this paper, we present a convolutional auto-encoder model that acts as a neural network alternative to convolutive NMF. Using the modeling flexibility granted by neural networks, we also explore the idea of using a Recurrent Neural Network in the encoder. Experimental results on speech mixtures from TIMIT dataset indicate that the convolutive architecture provides a significant improvement in separation performance in terms of BSSeval metrics.

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Shrikant Venkataramani, Y. Cem Subakan, Paris Smaragdis. 2017-09-20. Neural Network Alternatives to Convolutive Audio Models for Source Separation. https://arxiv.org/abs/1709.07908

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