A multiresolution non-negative tensor factorization approach for single channel sound source separation

• An adaptive time-frequency resolution based source separation approach is proposed.
• Non-negative tensor factorization is used for extracting the sources of a sound mixture.
• A joint optimization scheme is applied based on Kullback–Leibler (KL) divergence.
• The multiresolution information represented in each layer of the tensor is fused.
• The method enhances the quality of the separated sources.

We propose a single channel audio source separation method to alleviate the smearing effects caused by fixed time-frequency (TF) resolution Short-Time Fourier Transform (STFT). We introduce a multiresolution representation based on Non-negative Tensor Factorization (NTF) where each layer of the tensor represents the mixture signal at a different time-frequency resolution. In order to fuse the information at different layers, the source separation is modeled as a joint optimization problem where the optimal solution is derived based on the Kullback–Leibler (KL) divergence. The resynthesis is made through an additional adaptive weighted fusion procedure which combines the sources separated at different scales by maximizing energy concentration. Numerical results over a large sound database indicate that the proposed joint optimization scheme enhances the quality of the separated sources both in terms of the conventional and the perceptual distortion measures.