  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.
