A combination of semisoft and μ-law thresholding functions for enhancing noisy speech in wavelet packet domain

2012 ◽  
Author(s):  
Tahsina Farah Sanam ◽  
Celia Shahnaz
Keyword(s):  
Wavelet Packet ◽  
Noisy Speech

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.

Enhancement of noisy speech based on decision-directed Wiener approach in perceptual wavelet packet domain

2017 ◽  
Author(s):  
Md Tauhidul Islam ◽  
Mahtab Noor Shaan ◽  
Eashraque Jahan Easha ◽  
Ahmed Tahseen Minhaz ◽  
Celia Shahnaz ◽  
...  
Keyword(s):  
Wavelet Packet ◽  
Noisy Speech

Rayleigh modeling of teager energy operated perceptual wavelet packet coefficients for enhancing noisy speech

2017 ◽  
Vol 86 ◽  
pp. 64-74 ◽  
Author(s):  
Md Tauhidul Islam ◽  
Celia Shahnaz ◽  
Wei-Ping Zhu ◽  
M. Omair Ahmad
Keyword(s):  
Wavelet Packet ◽  
Noisy Speech ◽  
Teager Energy

scholarly journals Eigennoise Speech Recovery in Adverse Environments with Joint Compensation of Additive and Convolutive Noise

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Trung-Nghia Phung ◽  
Huy-Khoi Do ◽  
Van-Tao Nguyen ◽  
Quang-Vinh Thai
Keyword(s):  
Wavelet Packet ◽  
Voice Conversion ◽  
Noisy Environment ◽  
Noisy Environments ◽  
Noisy Speech ◽  
Alaryngeal Speech ◽  
Noise Robust ◽  
Low Dimensional ◽  
Recovery Approach ◽  
Adverse Environments

The learning-based speech recovery approach using statistical spectral conversion has been used for some kind of distorted speech as alaryngeal speech and body-conducted speech (or bone-conducted speech). This approach attempts to recover clean speech (undistorted speech) from noisy speech (distorted speech) by converting the statistical models of noisy speech into that of clean speech without the prior knowledge on characteristics and distributions of noise source. Presently, this approach has still not attracted many researchers to apply in general noisy speech enhancement because of some major problems: those are the difficulties of noise adaptation and the lack of noise robust synthesizable features in different noisy environments. In this paper, we adopted the methods of state-of-the-art voice conversions and speaker adaptation in speech recognition to the proposed speech recovery approach applied in different kinds of noisy environment, especially in adverse environments with joint compensation of additive and convolutive noises. We proposed to use the decorrelated wavelet packet coefficients as a low-dimensional robust synthesizable feature under noisy environments. We also proposed a noise adaptation for speech recovery with the eigennoise similar to the eigenvoice in voice conversion. The experimental results showed that the proposed approach highly outperformed traditional nonlearning-based approaches.

Detection of Cardiac Abnormality from PCG Signal Using Wavelet Packet

2015 ◽  
Vol 3 (1) ◽  
pp. 12-16
Author(s):  
Tripti Singh ◽  
◽  
Abhishek Misal ◽  
Keyword(s):  
Wavelet Packet ◽  
Cardiac Abnormality
Sign in / Sign up

Export Citation Format

Share Document