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Full-Text Articles in Signal Processing
Optimal Distributed Microphone Phase Estimation, Marek B. Trawicki, Michael T. Johnson
Optimal Distributed Microphone Phase Estimation, Marek B. Trawicki, Michael T. Johnson
Dr. Dolittle Project: A Framework for Classification and Understanding of Animal Vocalizations
This paper presents a minimum mean-square error spectral phase estimator for speech enhancement in the distributed multiple microphone scenario. The estimator uses Gaussian models for both the speech and noise priors under the assumption of a diffuse incoherent noise field representing ambient noise in a widely dispersed microphone configuration. Experiments demonstrate significant benefits of using the optimal multichannel phase estimator as compared to the noisy phase of a reference channel.
Auditory Coding Based Speech Enhancement, Yao Ren, Michael T. Johnson
Auditory Coding Based Speech Enhancement, Yao Ren, Michael T. Johnson
Dr. Dolittle Project: A Framework for Classification and Understanding of Animal Vocalizations
This paper demonstrates a speech enhancement system based on an efficient auditory coding approach, coding of time-relative structure using spikes. The spike coding method can more compactly represent the non-stationary characteristics of speech signals than the Fourier transform or wavelet transform. Enhancement is accomplished through the use of MMSE thresholding on the spike code. Experimental results show that compared with the spectral domain logSTSA filter, both the subjective spectrogram evaluation and objective SSNR improvement for the proposed approach is better in suppressing noise in high noise situations, with fewer musical artifacts.P
An Improved Snr Estimator For Speech Enhancement, Yao Ren, Michael T. Johnson
An Improved Snr Estimator For Speech Enhancement, Yao Ren, Michael T. Johnson
Dr. Dolittle Project: A Framework for Classification and Understanding of Animal Vocalizations
In this paper, we propose an MMSE a priori SNR estimator for speech enhancement. This estimator has similar benefits to the well-known decision-directed approach, but does not require an ad-hoc weighting factor to balance the past a priori SNR and current ML SNR estimate with smoothing across frames. Performance is evaluated in terms of estimation error and segmental SNR using the standard logSTSA speech enhancement method. Experimental results show that, in contrast with the decision-directed estimator and ML estimator, the proposed SNR estimator can help enhancement algorithms preserve more weak speech information and efficiently suppress musical noise.