Weighted codebook mapping for noisy speech enhancement using harmonic-noise model

2006 ◽  
Author(s):  
Esfandiar Zavarehei ◽  
Saeed Vaseghi ◽  
Qin Yan
Keyword(s):  
Speech Enhancement ◽  
Noise Model ◽  
Noisy Speech ◽  
Harmonic Noise

scholarly journals Kalman tracking of linear predictor and harmonic noise models for noisy speech enhancement

2008 ◽  
Vol 22 (1) ◽  
pp. 69-83 ◽  
Author(s):  
Qin Yan ◽  
Saeed Vaseghi ◽  
Esfandiar Zavarehei ◽  
Ben Milner ◽  
Jonathan Darch ◽  
...  
Keyword(s):  
Speech Enhancement ◽  
Linear Predictor ◽  
Noisy Speech ◽  
Noise Models ◽  
Harmonic Noise

A Deep Neural Network Based Harmonic Noise Model for Speech Enhancement

2018 ◽  
Author(s):  
Zhiheng Ouyang ◽  
Hongjiang Yu ◽  
Wei-Ping Zhu ◽  
Benoit Champagne
Keyword(s):  
Neural Network ◽  
Speech Enhancement ◽  
Deep Neural Network ◽  
Noise Model ◽  
Harmonic Noise

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.

Speech Enhancement Using Neuro-Fuzzy Classifier

2021 ◽  
pp. 164-181
Author(s):  
Judith Justin ◽  
Vanithamani R.
Keyword(s):  
Feature Extraction ◽  
Speech Enhancement ◽  
The Other ◽  
Objective Measures ◽  
Noise Levels ◽  
Fuzzy Classifier ◽  
Noisy Speech ◽  
Enhancement Technique ◽  
Time Frequency ◽  
Neuro Fuzzy

In this chapter, a speech enhancement technique is implemented using a neuro-fuzzy classifier. Noisy speech sentences from NOIZEUS and AURORA databases are taken for the study. Feature extraction is implemented through modifications in amplitude magnitude spectrograms. A four class neuro-fuzzy classifier splits the noisy speech samples into noise-only part, signal only part, more noise-less signal part, and more signal-less noise part of the time-frequency units. Appropriate weights are applied in the enhancement phase. The enhanced speech sentence is evaluated using objective measures. An analysis of the performance of the Neuro-Fuzzy 4 (NF 4) classifier is done. A comparison of the performance of the classifier with other conventional techniques is done for various noises at different noise levels. It is observed that the numerical values of the measures obtained are better when compared to the others. An overall comparison of the performance of the NF 4 classifier is done and it is inferred that NF4 outperforms the other techniques in speech enhancement.

The Energy Distribution of Tire-Road Noise and Noisy Speech Based on Wavelet Packet

2011 ◽  
Vol 464 ◽  
pp. 721-724 ◽  
Author(s):  
Zhi Yong He ◽  
Li Heng Luo
Keyword(s):  
Energy Distribution ◽  
Speech Enhancement ◽  
Mobile Communications ◽  
Wavelet Packet ◽  
Noise Signal ◽  
Wavelet Packets ◽  
Road Noise ◽  
Noisy Speech ◽  
Time Frequency ◽  
The Difference

Speech enhancement is very important for mobile communications or some other applications in car. The energy distribution of signal is the basis of algorithms which denoise noisy speech in time-frequency domain. In this work, the noise regarded is the tire-road noise when driving in expressway. Wavelet packets transform is used in the analysis. After decomposing noise signal and noisy speech signal by wavelet packet transform, the analysis for the difference of the energy distribution between noisy speech and noise is finished.

Multiple statistical models for soft decision in noisy speech enhancement

2007 ◽  
Vol 40 (3) ◽  
pp. 1123-1134 ◽  
Author(s):  
Joon-Hyuk Chang ◽  
Saeed Gazor ◽  
Nam Soo Kim ◽  
Sanjit K. Mitra
Keyword(s):  
Speech Enhancement ◽  
Statistical Models ◽  
Soft Decision ◽  
Noisy Speech
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