Speech Enhancement Using Perceptual Wavelet Packet Decomposition and Teager Energy Operator

Speech enhancement is a process which cultivates the quality of speech signal in noisy environment. It refers to removing or reducing the background noise in order to obtain an improved quality of original speech signal. Degradation of speech signal is most common problem in speech communication, so enhancement of the speech plays a vital role in improving the quality of speech signal. A number of methods are used for speech enhancement. Here we are using LPC based FEM in Wiener filter for speech enhancement. This method is then compared with several speech enhancement algorithms to obtain a better result or better speech quality. On comparing this method with other speech enhancement methods we obtain better speech performance. Here we are using NOIZEUS speech database in order to compare different speech enhancement methods. The experimental result shows that our proposed method provides better result and there is no information loss in original speech signal.

In real-world scenarios, speech signals are inevitably corrupted by various types of interference, making speech enhancement (SE) a critical task for robust speech processing. However, most existing SE methods only handle a limited range of distortions, such as additive noise, reverberation, or band limitation, while the study of SE under multiple simultaneous distortions remains limited. This gap affects the generalization and practical usability of SE methods in real-world environments. To address this gap, this paper proposes a novel Universal Discrete-domain SE model called UDSE. UDSE primarily works to enhance speech by predicting clean discrete tokens that are quantized by the residual vector quantizer (RVQ) of a pre-trained neural speech codec and are predicted in accordance with the RVQ rules. Specifically, UDSE first extracts global features from the degraded speech. Guided by these global features, the clean token prediction for each VQ follows the rules of RVQ, where the prediction of each VQ relies on the results of the preceding ones. Finally, the predicted clean tokens from all VQs are decoded to reconstruct the clean speech waveform. During training, the UDSE model employs a teacher-forcing strategy, and is optimized with cross-entropy loss. Experimental results confirm that the proposed UDSE model can effectively enhance speech degraded by various conventional and unconventional distortions, e.g., additive noise, reverberation, band limitation, clipping, phase distortion, and compression distortion, as well as their combinations. These results demonstrate the superior universality and practicality of UDSE compared to advanced regression-based SE methods.

The performance of thresholding-based methods for speech enhancement largely depends upon the estimation of the exact threshold value. In this paper, a new thresholding-based speech enhancement approach, where the threshold is statistically determined using the Teager energy-operated wavelet packet (WP) coefficients of noisy speech, is proposed. The threshold thus obtained is applied to the WP coefficients of the noisy speech by employing a semisoft thresholding function in order to obtain an enhanced speech. A number of simulations were carried out in the presence of white, car, pink, and multi-talker babble noises to evaluate the performance of the proposed method. Standard objective measures as well as subjective evaluations show that the proposed method is capable of outperforming the existing state-of-the-art thresholding-based speech enhancement approaches for noisy speech of high as well as low levels of SNR.

In this paper, we propose a novel speech enhancement (SE) method by exploiting the discrete wavelet transform (DWT). This new method reduces the amount of fast time-varying portion, viz. the DWT-wise detail component, in the spectrogram of speech signals so as to highlight the speech-dominant component and achieves better speech quality. A particularity of this new method is that it is completely unsupervised and requires no prior information about the clean speech and noise in the processed utterance. The presented DWT-based SE method with various scaling factors for the detail part is evaluated with a subset of Aurora-2 database, and the PESQ metric is used to indicate the quality of processed speech signals. The preliminary results show that the processed speech signals reveal a higher PESQ score in comparison with the original counterparts. Furthermore, we show that this method can still enhance the signal by totally discarding the detail part (setting the respective scaling factor to zero), revealing that the spectrogram can be down-sampled and thus compressed without the cost of lowered quality. In addition, we integrate this new method with conventional speech enhancement algorithms, including spectral subtraction, Wiener filtering, and spectral MMSE estimation, and show that the resulting integration behaves better than the respective component method. As a result, this new method is quite effective in improving the speech quality and well additive to the other SE methods.

Speech enhancement in noisy and reverberant conditions remains a challenging task. In this work, a time-frequency masking based method for speech enhancement with multi -channel data using convolutional neural networks (CNN) is proposed, where the CNN is trained to estimate the ideal ratio mask by discriminating directional speech source from diffuse or spatially uncorrelated noise. The proposed method operates on, frame-by-frame, the magnitude and phase components of the short-time Fourier transform coefficients of all frequency sub-bands and microphones. The avoidance of temporal context and explicit feature extraction makes the proposed method suitable for online implementation. In contrast to most speech enhancement methods that utilize multi -channel data, the proposed method does not require information about the spatial position of the desired speech source. Through experimental evaluation with both simulated and real data, we show the robustness of the proposed method to unseen acoustic conditions as well as varying noise levels.

The article presents a method for speech/pause segmentation based on energy analysis of speech signal fragments using the Teager Energy Operator and Short-Time Energy. The nature of segmentation is a linear division of a speech signal into fragments, calculation of energy characteristic of fragments using the Teager Energy Operator, calculation of Short-Time Energy values for fragments of energy characteristic, and determining `speech/pause' state of fragments based on the calculated threshold values of Short-Time Energy. The research on the proposed method was carried out, and the effectiveness of speech/pause segmentation was assessed in comparison with the method based on energy analysis using Short-Time Energy. In accordance with the obtained research results, it was concluded that due to the good susceptibility of the Teager Energy Operator to changes in signal amplitude and frequency, the proposed method provides the increased efficiency of speech/pause segmentation by 5.26 % and 5.51 % for the first and second kind errors, respectively. The proposed speech/pause segmentation method can be used for signal processing in real-time speech applications.

Speech is easily interfered by external environment in reality, which results in the loss of important features. Deep learning has become a popular speech enhancement method because of its superior potential in solving nonlinear mapping problems for complex features. However, the deficiency of traditional deep learning methods is the weak learning capability of important information from previous time steps and long-term event dependencies between the time-series data. To overcome this problem, we propose a novel speech enhancement method based on the fused features of deep neural networks (DNNs) and gated recurrent unit (GRU). The proposed method uses GRU to reduce the number of parameters of DNNs and acquire the context information of the speech, which improves the enhanced speech quality and intelligibility. Firstly, DNN with multiple hidden layers is used to learn the mapping relationship between the logarithmic power spectrum (LPS) features of noisy speech and clean speech. Secondly, the LPS feature of the deep neural network is fused with the noisy speech as the input of GRU network to compensate the missing context information. Finally, GRU network is performed to learn the mapping relationship between LPS features and log power spectrum features of clean speech spectrum. The proposed model is experimentally compared with traditional speech enhancement models, including DNN, CNN, LSTM and GRU. Experimental results demonstrate that the PESQ, SSNR and STOI of the proposed algorithm are improved by 30.72%, 39.84% and 5.53%, respectively, compared with the noise signal under the condition of matched noise. Under the condition of unmatched noise, the PESQ and STOI of the algorithm are improved by 23.8% and 37.36%, respectively. The advantage of the proposed method is that it uses the key information of features to suppress noise in both matched and unmatched noise cases and the proposed method outperforms other common methods in speech enhancement.

In this paper, we address the problem of speech enhancement by adaptive filtering algorithm. A particular attention has been made for the backward blind source separation (BBSS) algorithm and its use in speech enhancement application. In this paper, we propose to implement the BBSS algorithm in the Wavelet-domain. The proposed backward wavelet BBSS (WBBSS) algorithm is then used in speech enhancement test. The new WBBSS shows better performances in terms of convergence speed and steady state in comparison with BBSS one. The obtained results have been evaluated in terms of segmental SNR and cepstral distance criteria and confirm the best performance of the proposed WBBSS algorithm.

Speech enhancement (SE) methods mainly focus on recovering clean speech from noisy input. In real-world speech communication, however, noises often exist in not only speaker but also listener environments. Although SE methods can suppress the noise contained in the speaker’s voice, they cannot deal with the noise that is physically present in the listener side. To address such a complicated but common scenario, we investigate a deep learning-based joint framework integrating noise reduction (NR) with listening enhancement (LE), in which the NR module first suppresses noise and the LE module then modifies the denoised speech, i.e., the output of the NR module, to further improve speech intelligibility. The enhanced speech can thus be less noisy and more intelligible for listeners. Experimental results show that our proposed method achieves promising results and significantly outperforms the disjoint processing methods in terms of various speech evaluation metrics.

Many studies on deep learning-based speech enhancement (SE) utilizing the computational auditory scene analysis method typically employs the ideal binary mask or the ideal ratio mask to reconstruct the enhanced speech signal. However, many SE applications in real scenarios demand a desirable balance between denoising capability and computational cost. In this study, first, an improvement over the ideal ratio mask to attain more superior SE performance is proposed through introducing an efficient adaptive correlation-based factor for adjusting the ratio mask. The proposed method exploits the correlation coefficients among the noisy speech, noise and clean speech to effectively re-distribute the power ratio of the speech and noise during the ratio mask construction phase. Second, to make the supervised SE system more computationally-efficient, quantization techniques are considered to reduce the number of bits needed to represent floating numbers, leading to a more compact SE model. The proposed quantized correlation mask is utilized in conjunction with a 4-layer deep neural network (DNN-QCM) comprising dropout regulation, pre-training and noise-aware training to derive a robust and high-order mapping in enhancement, and to improve generalization capability in unseen conditions. Results show that the quantized correlation mask outperforms the conventional ratio mask representation and the other SE algorithms used for comparison. When compared to a DNN with ideal ratio mask as its learning targets, the DNN-QCM provided an improvement of approximately 6.5% in the short-time objective intelligibility score and 11.0% in the perceptual evaluation of speech quality score. The introduction of the quantization method can reduce the neural network weights to a 5-bit representation from a 32-bit, while effectively suppressing stationary and non-stationary noise. Timing analyses also show that with the techniques incorporated in the proposed DNN-QCM system to increase its compactness, the training and inference time can be reduced by 15.7% and 10.5%, respectively.

Most algorithms for speech enhancement in the spectral domain focus on the acquisition of an estimator of the clean speech parameter, such as spectrum or amplitude. Enhanced speech quality with these methods mainly depends upon the accuracy of the estimator. When the signal-to-noise ratio (SNR) becomes lower, e.g., less than 3 dB, the enhanced speech often shows unsatisfactory quality. Here, we propose a new method for speech enhancement in the spectral domain. It is based on our view that speech characteristics are perceived mainly by part of the spectral components which have higher local (or instantaneous) SNRs. These components have a special importance on speech SNR enhancement and speech understanding. Identifying these components in the noisy spectrum with a decision system and constructing enhanced speech just by the same noisy amplitude and phase, the result is similar to those popular algorithms. All that is needed is a local SNR decision for each spectral component. The threshold for the decision is derived and two popular methods for local SNR (LSNR) estimation are suggested. It shows better performance under lower SNR signals. Though the number of these kind of spectral components varies with different signal SNR, any algorithms which can pick up more than 90% of it is sufficient to match any existing methods. Primary considerations and results are shown.

本文提出了基于希尔伯特–黄变换(Hilbert-Huang Transform, HHT)与Teager能量算子(Teager Energy Operator, TEO)相融合的谐振接地选线新方法。首先对零序电流信号进行经验模态分解法(Empirical Mode De-composition, EMD)分解，然后将TEO合理的引入选线算法中，并与HHT极性判别法相融合，实现了HHT与TEO两者融合的优势互补。通过仿真分析，验证了所提方法的可行性和有效性。 The combination method of Hilbert-Huang Transform (HHT) and Teager Energy Operator (TEO) for fault line selection with resonance grounding is proposed in this paper. The zero sequence current signals are decomposed using Empirical Mode Decomposition (EMD). The TEO is reasonable introduced into algorithm of fault line selection. The TEO is combined with the method of HHT polarity estimate. The complementary advantage for combination method of HHT and TEO is implemented. The feasibility and effectiveness of the proposed method is verified by simulation results.

In this paper, a speech enhancement method based on noise compensation performed on short time magnitude spectra is presented following the geometric approach. Here the noise estimate to be subtracted from the noisy speech spectrum is proposed to be determined by exploiting the low frequency regions of noisy speech of the current frame rather than depending only on the initial silence frames. We argue that this approach of noise estimation offers the capability of tracking the time variation of the non-stationary noise. A noise estimation confidence parameter is also introduced to prevent the subtraction of the overestimated noise. A procedure is thus formulated to compensate for the distortion in the magnitude spectrum of the noisy speech signal. The noise compensated magnitude spectrum is then recombined with the noisy unchanged phase spectrum to produce a modified complex spectrum prior to synthesize an enhanced frame. Extensive simulations are carried out using NOIZEUS database in order to evaluate the performance of proposed method. It is shown in terms of objective measures, spectrogram analysis and informal subjective listening test that the proposed method consistently outperforms one of the state-of-the-art methods of speech enhancement from noisy speech corrupted by babble or car noise at high as well as very low levels of SNR.

Incipient fault feature extraction of rolling bearings based on the MVMD and Teager energy operator

Recently, deep neural networks (DNNs) have become the mainstream strategy for speech enhancement task because it can achieve the higher speech quality and intelligibility than the traditional methods. However, these DNN-based methods always need a large number of parallel corpus consisting of clean speech and noise to produce noisy data for the training of the DNN in order to improve the generalization of the network. As a result, this implies that many noisy speech signals that are collected in real environment cannot be used to train the DNN because of the lack of corresponding clean speech and noise. Additionally, as we know, noise varies with the time and scenario, so we cannot obtain parallel speech and noise due to infinite noise data and some limited speech data. Thus, the network training with unparallel speech and noise data is essential for the generalization of the network. To address this problem, we propose a novel parallel-data-free speech enhancement method, in which the cycle-consistent generative adversarial network (CycleGAN) and multi-objective learning are employed. Our method is also able to make best use of the benefits of multi-objective learning. On the training stage, we utilize two different encoders to encode the features of clean speech and noisy speech, respectively. Then, two forward generators are immediately used to predict the ideal time-frequency (T-F) mask and log-power spectrum (LPS) of clean speech. Two inverse generators are applied to map the magnitude spectrum (MS) and LPS of noisy speech, respectively. In addition, four discriminators are used to distinguish the real speech features from the generated features. Two encoders, four generators and four discriminators are simultaneously trained by using adversarial, identity-mapping, latent similarity and cycle-consistent loss. On the test stage, we directly utilize the forward generators and encoders to acquire the enhanced speech. The experimental results indicate that the proposed approach is able to achieve the better speech enhancement performance than the reference methods. Moreover, the proposed method is also effective to improve speech quality and intelligibility when the networks are trained under the parallel data.