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

Neural Network Alternatives to Convolutive Audio Models for Source Separation