Distance measures for signal processing and pattern recognition
Signal Processing
Dual-channel speech enhancement by superdirective beamforming
EURASIP Journal on Applied Signal Processing
Multichannel post-filtering in nonstationary noise environments
IEEE Transactions on Signal Processing
IEEE Transactions on Audio, Speech, and Language Processing
Noise Correlation Matrix Estimation for Multi-Microphone Speech Enhancement
IEEE Transactions on Audio, Speech, and Language Processing
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It is often very important for multichannel speech enhancement systems, such as hearing aids, to preserve spatial impressions. Usually, this is achieved by first designing a particular speech enhancement algorithm and later or separately constraining the obtained solution to respect spatial cues. Instead, we propose in this paper to conduct the entire system's design via the minimization of statistical spectral distances seen as functions of a real-valued, common gain to be applied to all channels in the frequency-domain. For various spectral distances, we show that the gain derived is expressible in terms of optimal multichannel spectral amplitude estimators (such as the multichannel Minimum Mean Squared Error Spectral Amplitude Estimator, among others). In addition, we report experimental results in complex environments (i.e., including reverberation, interfering talkers, and low signal-to-noise ratio), showing the potential of the proposed methods against recent state-of-the-art multichannel enhancement setups which preserve spatial cues as well.