A Mixing Matrix Estimation Algorithm for Speech Signals under the Under-Determined Blind Source Separation Model
The separation of speech signals has become a research
hotspot in the field of signal processing in recent years. It has
many applications and influences in teleconferencing, hearing aids,
speech recognition of machines and so on. The sounds received are
usually noisy. The issue of identifying the sounds of interest and
obtaining clear sounds in such an environment becomes a problem
worth exploring, that is, the problem of blind source separation.
This paper focuses on the under-determined blind source separation
(UBSS). Sparse component analysis is generally used for the problem
of under-determined blind source separation. The method is mainly
divided into two parts. Firstly, the clustering algorithm is used to
estimate the mixing matrix according to the observed signals. Then
the signal is separated based on the known mixing matrix. In this
paper, the problem of mixing matrix estimation is studied. This paper
proposes an improved algorithm to estimate the mixing matrix for
speech signals in the UBSS model. The traditional potential algorithm
is not accurate for the mixing matrix estimation, especially for low
signal-to noise ratio (SNR).In response to this problem, this paper
considers the idea of an improved potential function method to
estimate the mixing matrix. The algorithm not only avoids the inuence
of insufficient prior information in traditional clustering algorithm,
but also improves the estimation accuracy of mixing matrix. This
paper takes the mixing of four speech signals into two channels as
an example. The results of simulations show that the approach in this
paper not only improves the accuracy of estimation, but also applies
to any mixing matrix.
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