Single-Channel Blind Source Separation using Adaptive Mode Separation-Based Wavelet Transform and Density-Based Clustering with Sparse Reconstruction

• Published in: Circuits, systems, and signal processing systems, and signal processing, 2023-09, Vol.42 (9), p.5338-5357
• Main Authors: Kemiha, Mina, Kacha, Abdellah
• Format: Article
• Language: English
• Subjects: Circuits and Systems; Clustering; Density; Eigenvectors; Electrical Engineering; Electronics and Microelectronics; Engineering; Fourier transforms; Instrumentation; Reconstruction; Signal,Image and Speech Processing; Time-frequency analysis; Wavelet transforms
• ISSN: 0278-081X; 1531-5878
• DOI: 10.1007/s00034-023-02350-1

In this paper, the problem of single-channel blind source separation (SCBSS) is addressed using a novel approach that combines the adaptive mode separation-based wavelet transform (AMSWT) and the density-based clustering with sparse reconstruction. The proposed method is performed in the time–frequency domain and in a reverberant environment. First, using the Fourier transform, the amplitude spectrum of the observed mixture signal is obtained. Then, using variational scaling and wavelet functions, the AMSWT is used to adaptively extract spectral intrinsic components (SICs). To obtain a better time–frequency resolution, the AMSWT is applied to each mode. Thus, the SCBSS problem is transformed into a non-underdetermined. Then, for each frequency bin, the density-based clustering, reformulated to the eigenvector clustering problem, is performed to estimate the mixing matrix. Finally, sparse reconstruction is used to reconstruct the sources. The proposed method is evaluated using objective measures of separation quality. A computational complexity evaluation based on time consumption is also performed. Simulation results show that the proposed method is very effective for solving the SCBSS problem and provides better separation performances than the reference methods. However, the proposed method is computationally expensive.