A real-time blind source separation scheme and its application to reverberant and noisy acoustic environments

A Two-Channel Acoustic Front-End for Robust Automatic Speech Recognition in Noisy and Reverberant Environments

An acoustic front-end for robust automatic speech recognition in noisy and reverberant environments is proposed in this contribution. It comprises a blind source separation-based signal extraction scheme and only requires two microphone signals. The proposed front-end and its integration into the recognition system is analyzed and evaluated in noisy living room-like environments according to th...

Acoustic Blind Source Separation in Reverberant and Noisy Environments

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

The successful implementation of speech processing systems in the real world depends on its ability to handle adverse acoustic conditions with undesirable factors such as room reverberation and background noise. In this study, an extension to the established multiple sensors degenerate unmixing estimation technique (MENUET) algorithm for blind source separation is proposed based on the fuzzy c-...

Simultaneous speech recognition in noisy reverberant environme

In this paper, we examine the robustness of a Blind Signal Separation (BSS) technique in the time domain, based on a recurrent neural network, for separating multiple competing speakers in real reverberant environments. The separation network’s learning rule is based on the Maximum Likelihood Estimation criterion and was tested in real room situations in a noise-free and a noisy reverberant env...

Convolutive Blind Source Separation for Noisy Mixtures

The problem of separating convolutive mixtures of unknown time series arises in several application domains, a prominent example being the so-called cocktail party problem, where we want to recover the speech signals of multiple speakers who are simultaneously talking in a room. The room may be reverberant due to reflections on the walls, i.e., the original source signals sq(n), q = 1, . . . , ...