Abstract
Wavelet packets decompose signals in to broader components using linear spectral bisecting. Mixing matrix is the key issue in the Blind Source Separation (BSS) literature especially in under-determined cases. In this paper, we propose a simple and novel method in Short Time Wavelet Packet (STWP) analysis to estimate blindly the mixing matrix of speech signals from noise free linear mixtures in over-complete cases. In this paper, the Laplacian model is considered in short time-wavelet packets and is applied to each histogram of packets. Expectation Maximization (EM) algorithm is used to train the model and calculate the model parameters. In our simulations, comparison with the other recent results will be computed and it is shown that our results are better than others. It is shown that complexity of computation of model is decreased and consequently the speed of convergence is increased.
Highlights
Blind source separation (BSS) using Independent Component Analysis (ICA) has attracted great deal of attention in recent years
We propose a simple and novel method in Short Time Wavelet Packet (STWP) analysis to estimate blindly the mixing matrix of speech signals from noise free linear mixtures in over-complete cases
In Figure (8) we describe a new method based on short time analysis in wavelet packet domain
Summary
Blind source separation (BSS) using Independent Component Analysis (ICA) has attracted great deal of attention in recent years. Tinati et al proposed a new algorithm for selecting best wavelet packet node using LMM-EM model, they could obtain best results about estimation of mixing matrix [15,16]. They apply LMM model for speech mixture signals in wavelet packet domain using long-term Analysis. In their proposed algorithm because of long-term analysis, increasing the source number causes more errors in the estimation of mixing matrix. We will demonstrate that very promising results are obtained using examples
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
