We present a prototype system for Blind Source Separation (BSS) of many speech signals. Our system uses 8 microphones located at the vertexes of a 4cm×4cm×4cm cube and has the ability to separate signals distributed in three-dimensional space (Fig. 1). The mixed signals observed by the microphone array are processed by Independent Component Analysis (ICA)[1] in the frequency domain and separate...

Frequency domain Blind Source Separation (BSS) is shown to be equivalent to two sets of frequency domain adaptive microphone arrays, i.e., Adaptive Beamformers (ABF). The minimization of the off-diagonal components in the BSS update equation can be viewed as the minimization of the mean square error in the ABF. The unmixing matrix of the BSS and the filter coefficients of the ABF converge to th...

Degenerate Unmixing Estimation Technique (DUET) is a technique for blind source separation (BSS). Unlike the ICA based BSS techniques, DUET is a time-frequency scheme that relies on the socalled W-disjoint orthogonality (WDO) property of the source signals, which states that the windowed Fourier transforms of different source signals have statistically disjoint supports. In addition to being co...

Introduction In signal separation, multiple streams of information are extracted from linear mixtures of these signal streams. This process is blind if examples of the source signals, along with their corresponding mixtures are unavailable for training. Blind Signal Separation (BSS) is sometimes used interchangeably with independent component analysis (ICA), although technically, BSS and ICA ar...

In this paper, we address the problem of under-determined Blind Source Separation (BSS) of anechoic speech mixtures. We propose a demixing algorithm that exploits the sparsity of certain time-frequency expansions of speech signals. Our algorithm merges `-basis-pursuit with ideas based on the degenerate unmixing estimation technique (DUET) [1]. There are two main novel components to our approach...

For speech applications, blind source separation provides an efficient strategy to enhance the target signal and to reduce the background noise in a noisy environment. Most ICA-based blind source separation (BSS) algorithms are designed under the assumption that the target and interfering signals are spatially located. When the number of interfering signals is small, one of the BSS outputs is e...

The performance of Blind Source Separation (BSS) using Independent Component Analysis (ICA) declines significantly in a reverberant environment. The degradation is mainly caused by the residual crosstalk components derived from the reverberation of the jammer signal. This paper describes a post-processing method designed to refine output signals obtained by BSS. We propose a new method which us...

Frequency-domain blind source separation (BSS) is shown to be equivalent to two sets of frequency-domain adaptive beamformers (ABFs) under certain conditions. The zero search of the off-diagonal components in the BSS update equation can be viewed as the minimization of the mean square error in the ABFs. The unmixing matrix of the BSS and the filter coefficients of the ABFs converge to the same ...

Despite several recent proposals to achieve Blind Source Separation (BSS) for realistic acoustic signals, the separation performance is still not enough. In particular, when the length of an impulse response is long, the performance is highly limited. In this paper, we consider the reason for the poor performance of BSS in a long reverberation environment. First, we show that it is useless to b...

We propose a new overdetermined blind source separation (BSS) using frequency-domain independent component analysis (FDICA) based on multiple-input singleoutput (MISO) constraint. To achieve a superior separation performance under reverberant environments, we set the number of microphones to be larger than that of sources. This leads to alternative problems in which the sound qualities of the s...