Mask estimation incorporating phase-sensitive information for speech enhancement

Wang, Xianyun; Bao, Changchun

doi:10.1016/j.apacoust.2019.07.009

Cited by 13 publications

(5 citation statements)

References 37 publications

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“…From the results shown in Figure 4, it is clear that the values of STOI in (%) of the proposed method are higher than the existing methods. Thus the proposed method has the potential to suppress the distortion more and obtained the higher results for STOI compared to the existing methods 40 at higher SNR levels.…”

Section: Resultsmentioning

confidence: 91%

“…From Figure 5, it can be seen that the average SSNR of existing methods 40 are comparatively lesser than that of the suggested method. For the reduction in noise, phase‐sensitive information is incorporated with noisy signal, which enhances the speech intelligibility and quality of reconstructed signal in proposed method.…”

Section: Resultsmentioning

confidence: 93%

“…The better quality of speech signal is obtained when PESQ score is higher. It is evident from the Table 1 that, when the frequency‐sensitive detail is taken into consideration, the spectral structure of the temporal continuity of speech is possibly secured, leading to the improvement of the PESQ for the proposed method when compared with References 1‐3, BIRMP and OGAM of Reference 40.…”

Section: Resultsmentioning

confidence: 99%

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A novel double pole transfer function‐single frequency filtering approach for speech enhancement

Srinivasarao

Ghanekar

2020

Trans Emerging Tel Tech

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Speech intelligibility improvement is a major field of recent research with in audiology and hearing domain. Speech signal usually gets affected due to various environmental and background noise. Several approaches for speech intelligibility improvement were presented in the literature. In this paper, a method of speech enhancement based on Double Pole Transfer Function-Single Frequency Filter (DPTF-SFF) has been proposed. In the SFF approach the temporal and spectral resolutions are controlled with a one particular value of the filter that corresponds to the pole position in the complex z-plane. The proposed method uses double pole transfer function SFF that improves the magnitude and phase for various frequencies which are used in the synthesis of the original signal. In the analysis of this method clean speech signals are used from GRID corpus database and noise signals are taken from NOIZEUS and NOISEX-92 databases. The performance evaluation of this method is carried out in terms of Perceptual Evaluation of Speech Quality, Short-Time Objective Intelligibility, and Segmental Signal to Noise Ratio metrics. The implementation results proved that the proposed method is an efficient one as it gives higher intelligibility scores compared to the existing methods at different SNR levels (dB) and for different noises employed.

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Section: Resultsmentioning

confidence: 91%

Section: Resultsmentioning

confidence: 93%

Section: Resultsmentioning

confidence: 99%

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A novel double pole transfer function‐single frequency filtering approach for speech enhancement

Srinivasarao

Ghanekar

2020

Trans Emerging Tel Tech

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“…DNN-Based IRM algorithms [5][6][7][8] is also supervised speech enhancement algorithms, which have been proved as an effective solution on speech enhancement. For DNN-Based IRM algorithms, a DNN is usually used as the IRM estimator to predict the value of IRMs, from which amplitude spectrums of the noisy speeches will be corrected, and then, speech enhancement can be realized by obtaining the IDFT of the corrected amplitude spectrum combined with the phase information, we call these algorithms as single-estimator IRM algorithms.…”

Section: Introductionmentioning

confidence: 99%

SNR Classification Based Multi-Estimator IRM Speech Enhancement Algorithm

Xinqiang

Wang

Qin

et al. 2022

J. Phys.: Conf. Ser.

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Deep neural network(DNN)-based ideal ratio mask(IRM) estimation methods are often adopted in speech enhancement tasks. In the previous work, IRM estimation was usually realized by a single DNN-based IRM estimator without considering the SNR levels, which had a limited performance in real applications. Therefore, a two stage speech enhancement method is proposed in this paper. Firstly, a DNN-based SNR classifier is employed to classify the speech frames into three classes according to different SNR thresholds. Secondly, three corresponding DNN based IRM estimators related to the three SNR classes are trained respectively, from which the amplitude spectrum is corrected. Finally, speech enhancement is realized by doing IDFT to the corrected speech spectrum combined with the phase information of noisy speech. Experiment results show that the algorithm proposed in this paper has better performances in the evaluation of short time objective intelligibility(STOI), perceptual evaluation of speech quality(PESQ) and segmental signal-to-noise ratio improvement(SSNRI) scores.

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“…The research of speech separation technology plays a vital role in signal processing. Its purpose is to recover the original signal from the mixed signal generated by multiple sound sources [1]. Speech separation can effectively improve the perceptual quality of speech signals [2] or working as a frontend in automatic speech recognition systems to help improve the recognition accuracy [3].…”

Section: Introductionmentioning

confidence: 99%

Layered Convolutive Nonnegative Matrix Factorization for Speech Separation

Wang

Huang

et al. 2022

J. Phys.: Conf. Ser.

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Nonnegative matrix factorization (NMF) has attracted significant attention for its good performance in single-channel speech separation. The improved algorithms of NMF have become research hotspots. Layered NMF (LNMF), an improved algorithm, can express the source signal more accurately for its multilayer structure. However, LNMF sometimes performs poorly because it ignores the short-term correlation of speech signals. Based on LNMF and the advantages of Convolutive NMF (CNMF), we proposed a Layered Convolutive NMF(LCNMF) algorithm for single-channel speech separation. The LCNMF corporates the multilayer structure into the NMF and expands the convolution of the top-level NMF model. During the training, NMF is used to learn the non-top-level basis matrices, and CNMF is used to learn the top-level basis matrix, then combined with each single-layer of basis matrix. During the prediction, CNMF is used to separate mixed signals. The results on the dataset MIK-1K showed that LCNMF outperformed NMF and LNMF for separating the mixture of single-channel speech signals. LCNMF improved by 0.019, 1.049dB, 1.305dB, and 0.851dB on average compared with NMF, and improved by 0.007, 0.172dB, 0.090dB, and 0.366dB on average compared with LNMF in sort-term objective intelligibility (STOI), Source to Distortion Ratio (SDR), Source to Interference Ratio (SIR) and Source to Artifacts Ratio (SAR)

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Mask estimation incorporating phase-sensitive information for speech enhancement

Cited by 13 publications

References 37 publications

A novel double pole transfer function‐single frequency filtering approach for speech enhancement

A novel double pole transfer function‐single frequency filtering approach for speech enhancement

SNR Classification Based Multi-Estimator IRM Speech Enhancement Algorithm

Layered Convolutive Nonnegative Matrix Factorization for Speech Separation

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