Speech enhancement based on nonnegative matrix factorization in constant-Q frequency domain

Xu, Longting; Wei, Zhilin; Zaidi, Syed Faham Ali; Ren, Bo; Yang, Jichen

doi:10.1016/j.apacoust.2020.107732

Cited by 14 publications

(9 citation statements)

References 22 publications

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“…Compared to the existing models, the proposed method achieves higher SDR, PESQ, CORR, ESTOI, STOI, and SNR, as well as lower RMSE values. Moreover, the proposed model overcomes the drawbacks such as reduction in speech intelligibility [ 43 ], lower PESQ [ 40 ], lower robustness [ 37 ], not being suitable for complex noise environments [ 37 ], lower speech quality [ 10 ], and low SNR [ 45 ] [ 29 ]. However, this proposed method lacks at some noise sources and it did not determine the spectral magnitude and spectral phase estimation.…”

Section: Resultsmentioning

confidence: 99%

“…In 2021, Wei et al [ 40 ] have proposed a Constant Q Transform (CQT) intending to enhance the resolution of the lower frequency speech signals. The NMF/ Sparse NMF (SNMF) algorithm has been used in the backend.…”

Section: Literature Reviewmentioning

confidence: 99%

“… • There is no balance between residual noise and voice distortion. • Reduction in speech intelligibility Wei et al [ 40 ] Nonnegative matrix factorization ✓ Increases the low-frequency resolution of the speech ✓ Improved SNR ✓ Focused mainly on low-frequency signals • Lower denoise effect • Lower PESQ Zhou et al [ 32 ] DNN ✓ Improved objective perceptual quality ✓ Achieves a better PESQ score • Reduced noise suppression Chen et al [ 10 ] BiLSTM network ✓ Achieves good robustness against reverberations as well as distortion • Low consideration of speech quality measures …”

Section: Literature Reviewmentioning

confidence: 99%

“…Most studies have shown that reducing signal noise without distorting speech is a difficult challenge, which is one of the main reasons why perfect enhancement systems aren’t available. In this research, we focus the issues such as lower robustness, not suitable for complex noise conditions [ 37 ], more residual noise existing, lower SNR [ 45 ], reduction in speech intelligibility [ 43 ], lower denoise effect, lower PESQ [ 40 ], and low consideration of speech quality measures [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Speech enhancement using long short term memory with trained speech features and adaptive wiener filter

Garg

2022

Multimed Tools Appl

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Speech enhancement is the process of enhancing the clarity and intelligibility of speech signals that have been degraded due to background noise. With the assistance of deep learning, a novel speech signal enhancement model is introduced in this research. The proposed model is divided into two phases: (i) Training (ii) Testing. In the training phase, the noise spectrum and signal spectrum are estimated via a Non-negative Matrix Factorization (NMF) from the noisy input signal. Then, Empirical Mean Decomposition (EMD) features are extracted from the Wiener filter. The de-noised signal is acquired from EMD, the bark frequency is evaluated and the Fractional Delta AMS features are extracted. The key contribution of this study is the use of the Long Short Term Memory (LSTM) model to properly estimate the tuning factor η of the Wiener filter for all input signals. The LSTM was trained by the extracted features (EMD) via a modified wiener filter for decomposing the spectral signal and the output of EMD is the denoised enhanced speech signal. A comparative evaluation is carried out between the proposed and existing models in terms of error measures.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Speech enhancement using long short term memory with trained speech features and adaptive wiener filter

Garg

2022

Multimed Tools Appl

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“…The latest research [ 28 , 29 ] tends to convert sound signals into time–frequency representations (TFRs), such as short-time fourier transform (STFT) spectrograms and constant-Q transform (CQT) spectrograms, which depict the energy of the signal in different frequency bands over time. STFT spectrograms [ 30 ] are low in computation and offer frequency components in linearly spaced frequency bands; however, they are not conducive to highlighting spectral information in the low frequency range.…”

Section: Introductionmentioning

confidence: 99%

Percussion and PSO-SVM-Based Damage Detection for Refractory Materials

Yang

Peng

Zhou³

et al. 2023

Micromachines

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Refractory materials are basic materials widely used in industrial furnaces and thermal equipment. Their microstructure is similar to that of many heterogeneous high-performance materials used in micro/nanodevices. The presence of damage can reduce the mechanical properties and service life of refractory materials and even cause serious safety accidents. In this paper, a novel percussion and particle swarm optimization-support vector machine (PSO-SVM)-based method is proposed to detect damage in refractory materials. An impact is applied to the material and the generated sound is recorded. The percussion-induced sound signals are fed into a mel filter bank to generate time–frequency representations in the form of mel spectrograms. Then, two image descriptors—the local binary pattern (LBP) and histogram of oriented gradient (HOG)—are used to extract the texture information of the mel spectrogram. Finally, combining both HOG and LBP features, the fused features are input to the PSO-SVM algorithm to realize damage detection in refractory materials. The results demonstrated that the proposed method could identify five different degrees of damage of refractory materials, with an accuracy rate greater than 97%. Therefore, the percussion and PSO-SVM-based method proposed in this paper has high potential for field applications in damage detection in refractory material, and also has the potential to be extended to research on damage detection methods for other materials used in micro/nanodevices.

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