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.

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.

scholarly journals Wavelet Packet Transform based Speech Enhancement via Two-Dimensional SPP Estimator with Generalized Gamma Priors

2016 ◽  
Vol 41 (3) ◽  
pp. 579-590 ◽  
Author(s):  
Pengfei Sun ◽  
Jun Qin
Keyword(s):  
Speech Enhancement ◽  
Wavelet Packet ◽  
Minimum Mean Square Error ◽  
Wavelet Packet Transform ◽  
Perceptual Quality ◽  
Two Dimensional ◽  
Noisy Environments ◽  
Time Frequency ◽  
Generalized Gamma ◽  
Speech Model

Abstract Despite various speech enhancement techniques have been developed for different applications, existing methods are limited in noisy environments with high ambient noise levels. Speech presence probability (SPP) estimation is a speech enhancement technique to reduce speech distortions, especially in low signalto-noise ratios (SNRs) scenario. In this paper, we propose a new two-dimensional (2D) Teager-energyoperators (TEOs) improved SPP estimator for speech enhancement in time-frequency (T-F) domain. Wavelet packet transform (WPT) as a multiband decomposition technique is used to concentrate the energy distribution of speech components. A minimum mean-square error (MMSE) estimator is obtained based on the generalized gamma distribution speech model in WPT domain. In addition, the speech samples corrupted by environment and occupational noises (i.e., machine shop, factory and station) at different input SNRs are used to validate the proposed algorithm. Results suggest that the proposed method achieves a significant enhancement on perceptual quality, compared with four conventional speech enhancement algorithms (i.e., MMSE-84, MMSE-04, Wiener-96, and BTW).

Speech enhancement algorithm of improved OMLSA based on bilateral spectrogram filtering

2020 ◽  
Vol 39 (5) ◽  
pp. 6881-6889
Author(s):  
Jie Wang ◽  
Linhuang Yan ◽  
Jiayi Tian ◽  
Minmin Yuan
Keyword(s):  
Speech Enhancement ◽  
Visual Processing ◽  
Single Channel ◽  
Signal To Noise Ratio ◽  
Spectral Amplitude ◽  
Signal To Noise ◽  
Noisy Speech ◽  
Time Frequency ◽  
Perceptual Evaluation ◽  
Noise Ratio

In this paper, a bilateral spectrogram filtering (BSF)-based optimally modified log-spectral amplitude (OMLSA) estimator for single-channel speech enhancement is proposed, which can significantly improve the performance of OMLSA, especially in highly non-stationary noise environments, by taking advantage of bilateral filtering (BF), a widely used technology in image and visual processing, to preprocess the spectrogram of the noisy speech. BSF is capable of not only sharpening details, removing unwanted textures or background noise from the noisy speech spectrogram, but also preserving edges when considering a speech spectrogram as an image. The a posteriori signal-to-noise ratio (SNR) of OMLSA algorithm is estimated after applying BSF to the noisy speech. Besides, in order to reduce computing costs, a fast and accurate BF is adopted to reduce the algorithm complexity O(1) for each time-frequency bin. Finally, the proposed algorithm is compared with the original OMLSA and other classic denoising methods using various types of noise with different signal-to-noise ratios in terms of objective evaluation metrics such as segmental signal-to-noise ratio improvement and perceptual evaluation of speech quality. The results show the validity of the improved BSF-based OMLSA algorithm.

scholarly journals Feature extraction for fault diagnosis based on wavelet packet decomposition: An application on linear rolling guide

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879636 ◽  
Author(s):  
Hutian Feng ◽  
Rong Chen ◽  
Yiwei Wang
Keyword(s):  
Fault Diagnosis ◽  
Energy Distribution ◽  
Wavelet Packet ◽  
Numerical Control ◽  
Wavelet Packet Decomposition ◽  
Vibration Signals ◽  
Time Frequency ◽  
Good Ability ◽  
Rolling Guide ◽  
Lubrication Conditions

Linear rolling guide is increasingly being used as the transmission system in computer numerical control machine tools due to its high stiffness, low friction, good ability of precision retaining, and so on. The lubrication of rolling linear guide affects significantly its performance and hence monitoring the lubrication condition during its operation is of great importance. In this article, the relation between different lubrication conditions of linear rolling guide and their corresponding vibration signals is studied. Three lubrication conditions labeled as “Poor,”“Medium,” and “Good” are simulated to represent the actual working conditions. A data acquisition system is set up to acquire the vibration signals corresponding to different conditions. The wavelet packet decomposition is employed to perform time–frequency analysis of the raw signal, after which the energy distribution of the decomposed signals is extracted as the feature. Two linear rolling guides manufactured by different companies are used in the experiments. The results demonstrate that the relation between the energy distribution extracted from vibration signals and lubrication conditions follows a certain rule. A typical feedforward backpropagation neural network is used as the classifier to verify the effectiveness of energy distribution. The average classification accuracy of the network with energy distribution as input is more than 95%. The results show that the lubrication conditions can be characterized by “energy” hidden in the vibration signals and the energy distribution is an appropriate feature that can be used for fault diagnosis of linear rolling guide.

scholarly journals Speech enhancement methods based on binaural cue coding

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xianyun Wang ◽  
Changchun Bao
Keyword(s):  
Neural Network ◽  
Speech Enhancement ◽  
Speech Signal ◽  
Deep Neural Network ◽  
Experimental Results ◽  
Channel Correlation ◽  
Noisy Speech ◽  
Time Frequency ◽  
Improve Accuracy ◽  
Better Than

AbstractAccording to the encoding and decoding mechanism of binaural cue coding (BCC), in this paper, the speech and noise are considered as left channel signal and right channel signal of the BCC framework, respectively. Subsequently, the speech signal is estimated from noisy speech when the inter-channel level difference (ICLD) and inter-channel correlation (ICC) between speech and noise are given. In this paper, exact inter-channel cues and the pre-enhanced inter-channel cues are used for speech restoration. The exact inter-channel cues are extracted from clean speech and noise, and the pre-enhanced inter-channel cues are extracted from the pre-enhanced speech and estimated noise. After that, they are combined one by one to form a codebook. Once the pre-enhanced cues are extracted from noisy speech, the exact cues are estimated by a mapping between the pre-enhanced cues and a prior codebook. Next, the estimated exact cues are used to obtain a time-frequency (T-F) mask for enhancing noisy speech based on the decoding of BCC. In addition, in order to further improve accuracy of the T-F mask based on the inter-channel cues, the deep neural network (DNN)-based method is proposed to learn the mapping relationship between input features of noisy speech and the T-F masks. Experimental results show that the codebook-driven method can achieve better performance than conventional methods, and the DNN-based method performs better than the codebook-driven method.

Learning time-frequency mask for noisy speech enhancement using gaussian-bernoulli pre-trained deep neural networks

2021 ◽  
Vol 40 (1) ◽  
pp. 849-864
Author(s):  
Nasir Saleem ◽  
Muhammad Irfan Khattak ◽  
Mu’ath Al-Hasan ◽  
Atif Jan
Keyword(s):  
Neural Networks ◽  
Speech Enhancement ◽  
Speech Intelligibility ◽  
Deep Neural Networks ◽  
Training Data ◽  
Learning Approaches ◽  
Performance Gain ◽  
Noisy Speech ◽  
Time Frequency ◽  
Training Scheme

Speech enhancement is a very important problem in various speech processing applications. Recently, supervised speech enhancement using deep learning approaches to estimate a time-frequency mask have proved remarkable performance gain. In this paper, we have proposed time-frequency masking-based supervised speech enhancement method for improving intelligibility and quality of the noisy speech. We believe that a large performance gain can be achieved if deep neural networks (DNNs) are layer-wise pre-trained by stacking Gaussian-Bernoulli Restricted Boltzmann Machine (GB-RBM). The proposed DNN is called as Gaussian-Bernoulli Deep Belief Network (GB-DBN) and are optimized by minimizing errors between the estimated and pre-defined masks. Non-linear Mel-Scale weighted mean square error (LMW-MSE) loss function is used as training criterion. We have examined the performance of the proposed pre-training scheme using different DNNs which are established on three time-frequency masks comprised of the ideal amplitude mask (IAM), ideal ratio mask (IRM), and phase sensitive mask (PSM). The results in different noisy conditions demonstrated that when DNNs are pre-trained by the proposed scheme provided a persistent performance gain in terms of the perceived speech intelligibility and quality. Also, the proposed pre-training scheme is effective and robust in noisy training data.

The Properties of Multidimensional Semi-Orthogonal Frame Wavelet Packets

2010 ◽  
Vol 439-440 ◽  
pp. 896-901
Author(s):  
Qing Jiang Chen ◽  
Yu Ying Wang
Keyword(s):  
Matrix Theory ◽  
Wavelet Packet ◽  
Wavelet Packets ◽  
Analysis Method ◽  
Time Frequency ◽  
Dilation Matrix ◽  
Novel Method ◽  
Popular Subject ◽  
Vector Valued ◽  
Orthogonal Frame

Wavelet analysis has become a popular subject in scientific research during the past twenty years. In this work, we introduce the notion of vector-valued multiresolution analysis and vector-valued multivariate wavelet packets associated with an integer-valued dilation matrix. A novel method for constructing multi-dimen- -sional vector-valued wavelet packet is presented. Their characteristics are researched by means of operator theory, time-frequency analysis method and matrix theory. Three orthogonality formulas concerning the wavelet packets are established. Orthogonality decomposition relation formulas of the space are derived by constructing a series of subspaces of wavelet packets. Finally, one new orthonormal wavelet packet bases of are constructed from these wavelet packets.

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