Spectral Subtraction Steered by Multi-Step Forward Linear Prediction For Single Channel Speech Dereverberation

2006 ◽  
Author(s):  
K. Kinoshita ◽  
T. Nakatani ◽  
M. Miyoshi
Keyword(s):  
Linear Prediction ◽  
Single Channel ◽  
Spectral Subtraction ◽  
Speech Dereverberation

scholarly journals A Novel Scheme for Single-Channel Speech Dereverberation

Acoustics ◽  
2019 ◽  
Vol 1 (3) ◽  
pp. 711-725 ◽  
Author(s):  
Nikolaos Kilis ◽  
Nikolaos Mitianoudis
Keyword(s):  
Speech Enhancement ◽  
Background Noise ◽  
Linear Prediction ◽  
Single Channel ◽  
Matching Pursuit ◽  
Mixing Time ◽  
Speech Dereverberation ◽  
Degraded Speech ◽  
Reverberant Environments ◽  
Minimum Statistics

This paper presents a novel scheme for speech dereverberation. The core of our method is a two-stage single-channel speech enhancement scheme. Degraded speech obtains a sparser representation of the linear prediction residual in the first stage of our proposed scheme by applying orthogonal matching pursuit on overcomplete bases, trained by the K-SVD algorithm. Our method includes an estimation of reverberation and mixing time from a recorded hand clap or a simulated room impulse response, which are used to create a time-domain envelope. Late reverberation is suppressed at the second stage by estimating its energy from the previous envelope and removed with spectral subtraction. Further speech enhancement is applied on minimizing the background noise, based on optimal smoothing and minimum statistics. Experimental results indicate favorable quality, compared to two state-of-the-art methods, especially in real reverberant environments with increased reverberation and background noise.

scholarly journals Speech intelligibility enhancement for Thai-speaking cochlear implant listeners

2019 ◽  
Vol 13 (3) ◽  
pp. 866
Author(s):  
Siriporn Dachasilaruk ◽  
Niphat Jantharamin ◽  
Apichai Rungruang
Keyword(s):  
Cochlear Implant ◽  
Speech Enhancement ◽  
Speech Intelligibility ◽  
English Language ◽  
Single Channel ◽  
Spectral Subtraction ◽  
Monosyllabic Words ◽  
Listening Environments ◽  
Babble Noise ◽  
Vocoded Speech

Cochlear implant (CI) listeners encounter difficulties in communicating with other persons in noisy listening environments. However, most CI research has been carried out using the English language. In this study, single-channel speech enhancement (SE) strategies as a pre-processing approach for the CI system were investigated in terms of Thai speech intelligibility improvement. Two SE algorithms, namely multi-band spectral subtraction (MBSS) and Weiner filter (WF) algorithms, were evaluated. Speech signals consisting of monosyllabic and bisyllabic Thai words were degraded by speech-shaped noise and babble noise at SNR levels of 0, 5, and 10 dB. Then the noisy words were enhanced using SE algorithms. The enhanced words were fed into the CI system to synthesize vocoded speech. The vocoded speech was presented to twenty normal-hearing listeners. The results indicated that speech intelligibility was marginally improved by the MBSS algorithm and significantly improved by the WF algorithm in some conditions. The enhanced bisyllabic words showed a noticeably higher intelligibility improvement than the enhanced monosyllabic words in all conditions, particularly in speech-shaped noise. Such outcomes may be beneficial to Thai-speaking CI listeners.

Multi-Channel Linear Prediction-Based Speech Dereverberation With Sparse Priors

2015 ◽  
Vol 23 (9) ◽  
pp. 1509-1520 ◽  
Author(s):  
Ante Jukic ◽  
Toon van Waterschoot ◽  
Timo Gerkmann ◽  
Simon Doclo
Keyword(s):  
Linear Prediction ◽  
Speech Dereverberation ◽  
Sparse Priors

scholarly journals DeepLPC: A Deep Learning Approach to Augmented Kalman Filter-Based Single-Channel Speech Enhancement

2021 ◽  
Author(s):  
Sujan Kumar Roy ◽  
Aaron Nicolson ◽  
Kuldip K. Paliwal
Keyword(s):  
Deep Learning ◽  
Kalman Filter ◽  
Linear Prediction ◽  
Single Channel ◽  
Power Spectra ◽  
Learning Approaches ◽  
Learning Framework ◽  
Listening Tests ◽  
Whitening Filter ◽  
Prediction Coefficient

Current deep learning approaches to linear prediction coefficient (LPC) estimation for the augmented Kalman filter (AKF) produce bias estimates, due to the use of a whitening filter. This severely degrades the perceived quality and intelligibility of enhanced speech produced by the AKF. In this paper, we propose a deep learning framework that produces clean speech and noise LPC estimates with significantly less bias than previous methods, by avoiding the use of a whitening filter. The proposed framework, called DeepLPC, jointly estimates the clean speech and noise LPC power spectra. The estimated clean speech and noise LPC power spectra are passed through the inverse Fourier transform to form autocorrelation matrices, which are then solved by the Levinson-Durbin recursion to form the LPCs and prediction error variances of the speech and noise for the AKF. The performance of DeepLPC is evaluated on the NOIZEUS and DEMAND Voice Bank datasets using subjective AB listening tests, as well as seven different objective measures (CSIG, CBAK, COVL, PESQ, STOI, SegSNR, and SI-SDR). DeepLPC is compared to six existing deep learning-based methods. Compared to other deep learning approaches to clean speech LPC estimation, DeepLPC produces a lower spectral distortion (SD) level than existing methods, confirming that it exhibits less bias. DeepLPC also produced higher objective scores than any of the competing methods (with an improvement of 0.11 for CSIG, 0.15 for CBAK, 0.14 for COVL, 0.13 for PESQ, 2.66\% for STOI, 1.11 dB for SegSNR, and 1.05 dB for SI-SDR, over the next best method). The enhanced speech produced by DeepLPC was also the most preferred by listeners. By producing less biased clean speech and noise LPC estimates, DeepLPC enables the AKF to produce enhanced speech at a higher quality and intelligibility.

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