Enhancement of speech signal using diminished empirical mean curve decomposition-based adaptive Wiener filtering

Garg, Anil; Sahu, Omprakash

doi:10.1007/s10044-018-00768-x

Cited by 8 publications

(5 citation statements)

References 34 publications

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“…In this database, the five noise types, namely, “airport noise, exhibition noise, restaurant noise, station noise and street noise” are added to the speech signals. The performance of the proposed model (AR-GWO) is compared with the extant modelslike GA [ 29 ], PSO [ 20 ], ABC [ 24 ], FF [ 12 ] and GWO [ 14 ] in terms of “SDR, PESQ, SNR, RMSE, Correlation, ESTOI and CSED”. Also, statistical analysis and computational time analysis are performed.…”

Section: Resultsmentioning

confidence: 99%

“…The STFT coefficient of the frame γ is depicted as T ( γ , p ) and their mathematical formula is exhibited in Eq. ( 1 ) [ 14 ]. …”

Section: Processed Steps For Enhanced Speech Enhancementmentioning

confidence: 99%

“…In Eq. ( 2 ), the mean smoothened periodograms is represented as , and indicates the first-order recursive average of smoothened periodograms [ 14 ]. …”

Section: Processed Steps For Enhanced Speech Enhancementmentioning

confidence: 99%

“…The mathematical formula for “noisy speech’s magnitude spectrum” approximation, which is most commonly, utilized assumption for NMF-based processing of speech and audio signal, is show in Eq. ( 4 ) [ 14 ]. …”

Section: Processed Steps For Enhanced Speech Enhancementmentioning

confidence: 99%

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Adaptive Weiner filtering with AR-GWO based optimized fuzzy wavelet neural network for enhanced speech enhancement

Jadda

Prabha

2022

Multimed Tools Appl

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Speech signal enhancement is a subject of study in which a large number of researchers are working to improve the quality and perceptibility of speech signals. In the existing Kalman Filter method, the short-time magnitude or power spectrum due to random variations of noise was a serious problem and the signal-to-noise ratio was very low. This issue severely reduced the perceived qualityand intelligibility of enhanced speech. Thus, this paper intent to develop an improved speech enhancement model and it includes“training phase and testing phase”. In the training phase, the input noise corrupted signal is initially fed as input to both STFT-based noise estimation and NMF-based spectrum estimation forestimating the noise spectrum and signal spectrum, respectively. The obtained noise spectrum and the signal spectrum are fed as input to the Wiener filter and these filtered signals are subjected to Empirical Mean Decomposition (EMD).Since, tuning factor η plays a key role in Wiener filter, it has to be determined for each signal and from the denoised signal the bark frequency is evaluated. The computed bark frequency is fed as input to the learning algorithm referred as Fuzzy Wavelet Neural Network (FW-NN)for detecting the suited tuning factor η for the entire input signal in Wiener filter.An Adaptive Randomized Grey Wolf Optimization (AR-GWO) is proposed for proper tuning of the tuning factor η referred as tuned tuning factor ( η tuned ). The proposed AR-GWO is the improved version of standard Grey wolf optimization (GWO). In the testing phase, the training is accomplished initially and from which the tuning factor is gathered for each of the relevant input signal. Then, the properly tuned tuning factor ( η tuned ) from FW-NN is fed as input to EMD via adaptive wiener filter for decomposing the spectral signal and the output of EMD is denoised enhanced speech signal. At last, the performance of the adopted approach is evaluated to the existing approaches in terms of various metrics. In particular, the computation time of the adopted AR-GWO model is 34.07%, 43.57%, 28.86%, 38.88%, and 16.03% better than the existing GA, ABC, PSO, FF, and GWO approaches respectively.

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Section: Resultsmentioning

confidence: 99%

“…The STFT coefficient of the frame γ is depicted as T ( γ , p ) and their mathematical formula is exhibited in Eq. ( 1 ) [ 14 ]. …”

Section: Processed Steps For Enhanced Speech Enhancementmentioning

confidence: 99%

“…In Eq. ( 2 ), the mean smoothened periodograms is represented as , and indicates the first-order recursive average of smoothened periodograms [ 14 ]. …”

Section: Processed Steps For Enhanced Speech Enhancementmentioning

confidence: 99%

Section: Processed Steps For Enhanced Speech Enhancementmentioning

confidence: 99%

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Adaptive Weiner filtering with AR-GWO based optimized fuzzy wavelet neural network for enhanced speech enhancement

Jadda

Prabha

2022

Multimed Tools Appl

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“…This may involve techniques such as breath control [16], pitch and tone modifications [17,18], and articulation exercises [19], including spectral subtraction [20], Wiener filtering [21], and statistical prediction model-based [22] or machine learning-based approaches [23]. However, these traditional methods often suffer from drawbacks such as introducing artifacts, suppressing the desired speech components, or being computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals

Maskeliūnas,

Damaševičius,

Kulikajevas

et al. 2023

Cancers

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The problem of cleaning impaired speech is crucial for various applications such as speech recognition, telecommunication, and assistive technologies. In this paper, we propose a novel approach that combines Pareto-optimized deep learning with non-negative matrix factorization (NMF) to effectively reduce noise in impaired speech signals while preserving the quality of the desired speech. Our method begins by calculating the spectrogram of a noisy voice clip and extracting frequency statistics. A threshold is then determined based on the desired noise sensitivity, and a noise-to-signal mask is computed. This mask is smoothed to avoid abrupt transitions in noise levels, and the modified spectrogram is obtained by applying the smoothed mask to the signal spectrogram. We then employ a Pareto-optimized NMF to decompose the modified spectrogram into basis functions and corresponding weights, which are used to reconstruct the clean speech spectrogram. The final noise-reduced waveform is obtained by inverting the clean speech spectrogram. Our proposed method achieves a balance between various objectives, such as noise suppression, speech quality preservation, and computational efficiency, by leveraging Pareto optimization in the deep learning model. The experimental results demonstrate the effectiveness of our approach in cleaning alaryngeal speech signals, making it a promising solution for various real-world applications.

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

Garg

2022

Multimed Tools Appl

Self Cite

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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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Enhancement of speech signal using diminished empirical mean curve decomposition-based adaptive Wiener filtering

Cited by 8 publications

References 34 publications

Adaptive Weiner filtering with AR-GWO based optimized fuzzy wavelet neural network for enhanced speech enhancement

Adaptive Weiner filtering with AR-GWO based optimized fuzzy wavelet neural network for enhanced speech enhancement

Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals

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

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