An efficient speech enhancement method using kalman filter and spectral subtraction

2005 ◽  
Author(s):  
Chi-Chou Kao ◽  
Yen-Tai Lai
Keyword(s):  
Kalman Filter ◽  
Speech Enhancement ◽  
Spectral Subtraction ◽  
Enhancement Method

A Novel Speech Enhancement Method Using Fourier Series Decomposition and Spectral Subtraction for Robust Speaker Identification

2019 ◽  
Vol 108 (2) ◽  
pp. 1055-1068 ◽  
Author(s):  
Ali I. Siam ◽  
Heba A. El-khobby ◽  
Mustafa M. Abd Elnaby ◽  
Hatem S. Abdelkader ◽  
Fathi E. Abd El-Samie
Keyword(s):  
Fourier Series ◽  
Speech Enhancement ◽  
Speaker Identification ◽  
Spectral Subtraction ◽  
Enhancement Method ◽  
Robust Speaker Identification

scholarly journals Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement

Signals ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 434-455
Author(s):  
Sujan Kumar Roy ◽  
Kuldip K. Paliwal
Keyword(s):  
Kalman Filter ◽  
Speech Enhancement ◽  
Linear Prediction ◽  
Real Life ◽  
Model Parameters ◽  
Noise Variance ◽  
Noisy Speech ◽  
Kalman Gain ◽  
Whitening Filter ◽  
Prediction Coefficient

Inaccurate estimates of the linear prediction coefficient (LPC) and noise variance introduce bias in Kalman filter (KF) gain and degrade speech enhancement performance. The existing methods propose a tuning of the biased Kalman gain, particularly in stationary noise conditions. This paper introduces a tuning of the KF gain for speech enhancement in real-life noise conditions. First, we estimate noise from each noisy speech frame using a speech presence probability (SPP) method to compute the noise variance. Then, we construct a whitening filter (with its coefficients computed from the estimated noise) to pre-whiten each noisy speech frame prior to computing the speech LPC parameters. We then construct the KF with the estimated parameters, where the robustness metric offsets the bias in KF gain during speech absence of noisy speech to that of the sensitivity metric during speech presence to achieve better noise reduction. The noise variance and the speech model parameters are adopted as a speech activity detector. The reduced-biased Kalman gain enables the KF to minimize the noise effect significantly, yielding the enhanced speech. Objective and subjective scores on the NOIZEUS corpus demonstrate that the enhanced speech produced by the proposed method exhibits higher quality and intelligibility than some benchmark methods.

A Speech Enhancement Method Using Attention Mechanism and Gated Recurrent Unit

2021 ◽  
Author(s):  
Kaibei Peng ◽  
Xiaoming Sun ◽  
Haowei Chen ◽  
Zhen He ◽  
Jianrong Wang
Keyword(s):  
Speech Enhancement ◽  
Attention Mechanism ◽  
Enhancement Method ◽  
Gated Recurrent Unit

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.

scholarly journals Speech Enhancement by Spectral Subtraction Method

2014 ◽  
Vol 96 (13) ◽  
pp. 45-48 ◽  
Author(s):  
Kaladharan N
Keyword(s):  
Speech Enhancement ◽  
Spectral Subtraction ◽  
Subtraction Method ◽  
Spectral Subtraction Method
Sign in / Sign up

Export Citation Format

Share Document