An adaptive speech enhancement approach based on DCT and empirical mode decomposition

Rao, Shanshan; Sankar, K. Jaya; Naidu, C. D.

doi:10.1109/iccsp.2016.7754206

Cited by 2 publications

(11 citation statements)

References 29 publications

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“…The obtained results for various noises at various SNR levels reveal the outstanding performance of proposed approach. In the case of Babble noise, on an average the proposed approach achieved an improvement of 2.8789 dB and 1.0777 dB in the Output SNR compared with the Tafiq.et.al [12] and S.N.Rao et.al [15] respectively. Similarly the improvement is observed to be 7.1816 dB and .4517 dB for Airport Noise.…”

Section: Resultsmentioning

confidence: 85%

“…From the obtained Output AvgSegSNR values, the proposed approach is observed to be achieved an improvement of 4.9626 dB and 0.4935 dB from the conventional approaches Tafiq.et.al [12] and S.N.Rao et.al [15] respectively. Further the obtained results through PESQ test also reveal an enhancement of 2.0861 and 0.2732 approximately from the conventional approaches.…”

Section: Resultsmentioning

confidence: 92%

“…The following tables describe the details of performance measures for all test cases. approach is also observed to be high compared with Tafiq.et.al [12] and S.N.Rao et.al [15]. Further to provide a better idea about the speech quality of enhanced speech signals, the enhanced speech signals are subjected to Perceptual evaluation of speech quality (PESQ) test.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The quality of the enhanced speech signal through the proposed approach was evaluated through Perceptual evaluation of speech quality (PESQ) test. To show the performance improvement of proposed speech enhancement algorithm, it was compared with conventional approaches proposed by Tafiq Hasan et.al [12] and the S.N.Rao et.al [15].…”

Section: Simulation Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Suppression of Non-Stationary Noises Through the Generalized Signal Detector

Saggurti¹,

Reddy²,

Challa³

2018

IJIES

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This paper presents an adaptive speech enhancement approach to suppress non-stationary noises form a noisy speech signals. This approach is based on the Empirical Mode Decomposition and Signal Uncertainty. The EMD is just used as pre-processor for signal decomposition into Intrinsic Mode Functions. Further The IMFs are processed for noise suppression through a recursive smoothing based on a smoothing factor which was decided based on the probability of speech presence. A new signal detector is proposed here to measure the probability of speech presence. This approach mainly focused on the optimization of probability of detection through the newly proposed signal detector. The simulation is carried out through various speech signals contaminated with different noise types like White noise, Babble noise and Airport noise at various SNR levels reveals the outstanding performance of proposed approach. The performance evaluation is carried out by measuring the performance metrics, Overall output SNR, Output AvgSegSNR and Perceptual Evaluation of Speech Quality (PESQ). The evaluation is carried out for varying noise strengths and for every test case all these metrics are evaluated and compared with conventional approaches.

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Section: Resultsmentioning

confidence: 85%

Section: Resultsmentioning

confidence: 92%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Suppression of Non-Stationary Noises Through the Generalized Signal Detector

Saggurti¹,

Reddy²,

Challa³

2018

IJIES

View full text Add to dashboard Cite

show abstract

“…On the other hand, the thresholding based noise suppression is also attained a much effective results in the speech enhancement research [21], [22]. Different preprocessing approaches including the wavelet transform [24], Empirical Mode decomposition [24], Discrete Cosine transform [23] etc., are applied over the speech signal to study the time-frequency characteristics and then deriving a novel threshold to suppress the external noise in the noise contaminated speech signal. A new probabilistic speech enhancement filter is presented in [25] considering the three state possibilities of discrete cosine transform (DCT) coefficients of noisy speech: speech absence, speech and noise are constructive, and destructive.…”

Section: Literature Surveymentioning

confidence: 99%

An Improved Bi-Level Thresholding Based Uncertainty Evaluation for Speech Enhancement in Non-Stationary Noises

Rao¹,

Sankar²,

Naidu³

2018

IJET

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This paper proposes a new speech enhancement framework to improve the quality of speeches recorded under adverse acoustic environments based on the speech presence uncertainty. Since the uncertainty evaluation gives a more and clear discrimination about the speech and noise, this paper proposes a new uncertainty evaluation mechanism as a preprocessing mechanism to the noise suppression methods. This mechanism relates with energies of a noisy speech signal and classifies the speech segments and noise segments more perfectly. In addition to the quality enhancement, this approach also reduces the unnecessary computational burden over the speech processing system. Extensive simulations are carried out over the speech signals with different types of non-stationary noises like babble noise, exhibition noise, restaurant noise and train station noises and the performance is measured with the performance metrics namely the Output SNR, AvgSegSNR, PESQ and COMP. The comparative analysis of proposed approach over the conventional approaches shows an outstanding performance in all environments.

show abstract

An adaptive speech enhancement approach based on DCT and empirical mode decomposition

Cited by 2 publications

References 29 publications

Suppression of Non-Stationary Noises Through the Generalized Signal Detector

Suppression of Non-Stationary Noises Through the Generalized Signal Detector

An Improved Bi-Level Thresholding Based Uncertainty Evaluation for Speech Enhancement in Non-Stationary Noises

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