EURASIP Journal on Advances in Signal Processing - Special issue on digital signal processing for hearing instruments
Low complexity DFT-domain noise PSD tracking using high-resolution periodograms
EURASIP Journal on Advances in Signal Processing
Superposition frames for adaptive time-frequency analysis and fast reconstruction
IEEE Transactions on Signal Processing
Improving performance of a noise reduction algorithm by switching the analysis filter bank
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
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Single-channel enhancement algorithms are widely used to overcome the degradation of noisy speech signals. Speech enhancement gain functions are typically computed from two quantities, namely, an estimate of the noise power spectrum and of the noisy speech power spectrum. The variance of these power spectral estimates degrades the quality of the enhanced signal and smoothing techniques are, therefore, often used to decrease the variance. In this paper, we present a method to determine the noisy speech power spectrum based on an adaptive time segmentation. More specifically, the proposed algorithm determines for each noisy frame which of the surrounding frames should contribute to the corresponding noisy power spectral estimate. Further, we demonstrate the potential of our adaptive segmentation in both maximum likelihood and decision direction-based speech enhancement methods by making a better estimate of the a priori signal-to-noise ratio (SNR) xi. Objective and subjective experiments show that an adaptive time segmentation leads to significant performance improvements in comparison to the conventionally used fixed segmentations, particularly in transitional regions, where we observe local SNR improvements in the order of 5 dB