Single-Channel Signal Separation Using Spectral Basis Correlation with Sparse Nonnegative Tensor Factorization

A novel approach for solving the single-channel signal separation is presented the proposed sparse nonnegative tensor factorization under the framework of maximum a posteriori probability and adaptively fine-tuned using the hierarchical Bayesian approach with a new mixing mixture model. The mixing mixture is an analogy of a stereo signal concept given by one real and the other virtual microphones. An “imitated-stereo” mixture model is thus developed by weighting and time-shifting the original single-channel mixture. This leads to an artificial mixing system of dual channels which gives rise to a new form of spectral basis correlation diversity of the sources. Underlying all factorization algorithms is the principal difficulty in estimating the adequate number of latent components for each signal. This paper addresses these issues by developing a framework for pruning unnecessary components and incorporating a modified multivariate rectified Gaussian prior information into the spectral basis features. The parameters of the imitated-stereo model are estimated via the proposed sparse nonnegative tensor factorization with Itakura–Saito divergence. In addition, the separability conditions of the proposed mixture model are derived and demonstrated that the proposed method can separate real-time captured mixtures. Experimental testing on real audio sources has been conducted to verify the capability of the proposed method

Extension of DUET to single-channel mixing model and separability analysis

In this paper, the DUET binaural model is extended to the single-channel mixing model where only one microphone is available for recording. A novel “artificial stereo” mixing model is proposed to create a synthetic stereo signal by weighting and time-shifting the original single-channel mixture. Separability analysis of the proposed model has also been derived to verify that the artificial stereo mixture is separable. This work, therefore, relaxes the under-determined ill-conditions associated with monaural source separation and path the way for binaural source separation approaches to solve monaural mixture

Online Noisy Single-Channel Source Separation Using Adaptive Spectrum Amplitude Estimator and Masking

A novel single-channel source separation method is presented to recover the original signals given only a single observed mixture in noisy environment. The proposed separation method is an online adaptive process and independent of parameters initialization. In this paper, a noisy pseudo-stereo mixing model is developed by formulating an artificial mixture from the observed mixture where the signals are modeled by the autoregressive process. The proposed demixing process composes of two steps: First, the noisy mixing model is enhanced by selecting the time-frequency (TF) units of signal presence and computing the mixture spectral amplitude, and second, an adaptive estimation of the parameters associated with each source is computed frame-by-frame, which is then used to construct a TF mask for the separation process. To assess the performance of the proposed method, noisy mixtures of real-audio sources with nonstationary noise have been conducted under various SNRs. Experiments show that the proposed algorithm has yielded superior separation performance especially in low input SNR compared with existing methods

Performance improvement in single-channel blind separation with pseudo-stereo mixture using cochleagram representation based masks

A novel single-channel blind source separation (SCBSS) algorithm using Cochleagram-mask based technique is presented in this paper. The proposed system offers benefits such as resemblance of a stereo signal concept given by one microphone, independent of initialization and a priori knowledge of the sources, improved performance with sources which do not strictly satisfy the windowed-disjoint orthogonality (WDO) condition and reduced computational complexity without the need for iterative optimizations. The separation process comprises three steps: 1) estimation of source characteristics, where the source signals are modelled by the autoregressive process and 2) construction of masks using only the single-channel mixture 3) binary TF masks modification through cochleagram processing 4) improve separation depth through independent component analysis (ICA). Experiment results revealed at least 3dB signal to interference ratio comparing to previous systems [1]