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ABSTRACT: In an environment with multiple audio sources, blind source separation (BSS) makes use of multiple microphone signals to estimate the respective source signals. Under normal circumstances, it is not possible to completely “unmix” the audio sources. One technique to further improve the system performance is to use all BSS outputs to generate a Wiener filter that is then applied to the desired output. The Wiener post processing improves the signal-to-interference ratio (SIR) but we show that it does not necessarily improve the perceptual quality of the BSS output. By using a perceptually inspired signal enhancement method on the BSS output signals, a significant improvement in quality can be achieved. Results are shown for recordings made in a noisy office environment using two microphones mounted on a cell phone.
Digital Signal Processing Workshop and IEEE Signal Processing Education Workshop (DSP/SPE), 2011 IEEE; 02/2011
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ABSTRACT: For cell-phone applications, single microphone noise suppression techniques have limited performance at very low SNR (close to 0 dB). In certain cases, they also suffer from the artifacts of nonlinear processing. In this paper, we will show that techniques based on two-microphone blind source separation (BSS) algorithm provide significant interference suppression for cell-phone applications, particularly at low operating SNR values. We also propose a post-BSS processing method based on frequency-domain spectral subtraction that further improves the BSS speech output in diffused noise case. We optimize the BSS algorithm so that it can be implemented on a low-power audio codec processor, which would be ideal for cell phone applications. Furthermore, based on extensive analysis under different noise and acoustic conditions, we suggest recommendations for optimal placement of microphones on a cell phone. We also study the trade-off between the unmixing filter length and the noise suppression performance. In all our experiments, we use real recordings made on a cell phone equipped with two microphones.
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on; 04/2010 · 4.63 Impact Factor
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ABSTRACT: In this paper we describe a technique that uses adaptive gain control to achieve noise suppression in speech signals. The method used to map the dynamic range of the signal is based on the human auditory perceptual model. Since the processing is based on the model of human perception, the resulting noise suppressed speech is natural sounding. The computational complexity of the proposed method is low and the mapping of the dynamic range of the signal has a low delay. Because of these properties, this method is ideal for real-time implementation.
Applications of Signal Processing to Audio and Acoustics, 2009. WASPAA '09. IEEE Workshop on; 11/2009
