K-means Based Underdetermined Blind Speech Separation

Abstract

Abstract. This chapter addresses a blind sparse source separation method that can employ arbitrarily arranged multiple microphones. Some sparse source separation methods, which rely on source sparseness and an anechoic mixing model, have already been proposed. The validity of the sparseness and anechoic assumptions will be investigated in this chapter. As most of the existing methods utilize a stereo (two sensors) system, they limit the separation ability to a 2-dimensional half-plane. This chapter describes a method for multiple microphones. This method employs the k-means algorithm, which is an efficient clustering algorithm. The method can be easily applied to three or more sensors arranged nonlinearly. Promising results were obtained for 2-and 3-dimensionally distributed speech signals with nonlinear/nonuniform sensor arrays in a real room even in underdetermined situations.

Evaluations on underdetermined blind source separation in adverse environments using time-frequency masking

“…To estimate such TF points, a spatial feature vector is calculated from the STFT representations of the M observations. Previous researches [14,15] have identified level ratios and phase differences between the observations as appropriate features, as such features retain information on the magnitude and the argument of the TF points. Further discussion is presented in section 4.3.1.…”

“…Whilst remaining similar in spirit to the DUET, the research was inclusive of non-ideal conditions such as room reverberation, and allowed more than two sensors in an arbitrary arrangement. This eventually culminated in the development of the multiple sensors degenerate unmixing estimation technique, termed MENUET [14,15]. Additionally, the mask estimation in MENUET was automated through the application of the k-means clustering technique.…”

“…Firstly, this study proposes that the substitution of the TF clustering stage with a fuzzy clustering approach as explored in [20,21] will improve the separation performance in the same conditions as presented in [14,15]. Secondly, it is hypothesized that this combination is sufficiently robust to withstand the degrading effects of reverberation and environmental noise, and evaluations of all the methods under the challenging conditions of reverberation and environmental background noise are presented.…”

Informed Source Separation of Linear Instantaneous Under-Determined Audio Mixtures by Source Index Embedding

“…The relative attenuation and delay histograms are used to determine the mixing parameters, before the source signals are estimated by TF binary masking. This approach is extended to more than 2 microphones in [11] [12].…”

“…11.5). Therefore the impact of the noise due to the watermarking and the sparse assumption on the inversion process (see Section III-C) also remains limited 11 .…”