This paper proposes a two-stage algorithm to separate two sound sources by using matched beamforming and time-frequency masking techniques. At first, beamforming was used to separate the sound mixtures back to the original sources while preserving the original contents to the maximum extent. The residual interference was then suppressed by the time-frequency masking technique. A sequential least squares method was used in developing a matched beamformer to estimate the relative transfer function (RTF). From experimental results, it has been shown that the proposed method exhibits improved performance in sound source separation compared to conventional methods. Signal enhanced factor (SEF) was improved by an average of 8.39 dB over the baseline.

This paper proposes a combined scheme in which adaptive beamforming and acoustic echo canceller are integrated in order to achieve a robust full-duplex speech interface between user and mobile robot. In particular, this paper addresses the situation in which an echo signal and the userpsilas voice are propagated from the same direction toward a linear array of microphones. We propose a cascading scheme that uses an acoustic echo canceller followed by an adaptive beamformer. By using the system output to adapt echo canceller, and by controlling the adaptive mode of each adaptive filter in the beamformer and in the echo canceller, we can recover the original speech, which would otherwise have remained contaminated by both noise and echoes. In addition, a double-talk detector is proposed so that effective acoustic echo cancellation may be attained without generating error divergence when the userpsilas voice and the echo are present simultaneously. Representative experimental results with real data demonstrate the validity of the proposed scheme.

This paper introduces a home robot application of multi-channel based spatial noise reduction for creating human-robot speech interfaces. A microphone array is employed first to create a speech-only directional conduit, which is realized through adaptive beamforming. Through the directional conduit, the intended speech signal from the desired direction is processed for detection and recognition, while unintended speech-like-sources or undesirable noise from other angles is suppressed. If speech signal is absent among the incoming signals through the conduit, further attenuation of undesirable signals is achieved by using a spatial noise reduction filter. Experimental validation of the technique was conducted using a computer simulation and also an online Samsung AnyBot test. Although the environments exhibited highly non-stationary noise, the method achieved an average speech recognition rate of 87.4% in the case of the computer simulation and 81.6% for the online Samsung AnyBot test. From the cases tested so far, the proposed implementation seems to be effective for practical robot applications in highly non-stationary noise environment.