An evaluation of noise power spectral density estimation algorithms in adverse acoustic environments

Taghia, Jalal; Taghia, Jalil; Mohammadiha, Nasser; Sang, Jinqiu; Bouse, Vaclav; Martin, Rainer

doi:10.1109/icassp.2011.5947389

Cited by 47 publications

(31 citation statements)

References 8 publications

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“…Some examples are the discrete Fourier transform (DFT)-subspace approach [19], or minimum mean-square error (MMSE)-based approaches [20] [21]. Although DFTsubspace-based approaches lead to quite some improvement for non-stationary noise sources compared to, e.g., MS-based spectral noise power estimators [22], computationally they are rather demanding. The MMSE-based algorithm [21] on the other hand is computationally much less demanding and at the same time robust to increasing noise levels as shown in a comparison presented in [22].…”

Section: Introductionmentioning

confidence: 99%

Unbiased MMSE-Based Noise Power Estimation With Low Complexity and Low Tracking Delay

Gerkmann

Hendriks

2012

IEEE Trans. Audio Speech Lang. Process.

478

405

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Abstract-Recently, it has been proposed to estimate the noise power spectral density by means of minimum mean-square error (MMSE) optimal estimation. We show that the resulting estimator can be interpreted as a voice activity detector (VAD)-based noise power estimator, where the noise power is updated only when speech absence is signaled, compensated with a required bias compensation. We show that the bias compensation is unnecessary when we replace the VAD by a soft speech presence probability (SPP) with fixed priors. Choosing fixed priors also has the benefit of decoupling the noise power estimator from subsequent steps in a speech enhancement framework, such as the estimation of the speech power and the estimation of the clean speech. We show that the proposed SPP approach maintains the quick noise tracking performance of the bias compensated MMSE-based approach while exhibiting less overestimation of the spectral noise power and an even lower computational complexity.Index Terms-Noise power estimation, speech enhancement.

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Section: Introductionmentioning

confidence: 99%

Unbiased MMSE-Based Noise Power Estimation With Low Complexity and Low Tracking Delay

Gerkmann

Hendriks

2012

IEEE Trans. Audio Speech Lang. Process.

478

405

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“…In a practical scenario the noise statistics would need to be computed using, for example, voice activity detection, minimum statistics or recursive averaging techniques (Martin, 2001; Rangachari and Loizou, 2006;Taghia et al, 2011). We chose to use a simple method as the noise estimate is reliable which means subsequent analysis of acoustic feature estimation is not subject to variations in noise estimation accuracy.…”

Section: Methodsmentioning

confidence: 99%

Estimating acoustic speech features in low signal-to-noise ratios using a statistical framework

Harding

Milner

2017

Computer Speech & Language

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“…In a second step, a noise PSD estimation algorithm based on the minimum mean square error (MMSE) by Hendriks et al [11] was used to estimate the noise PSD of noisy speech in each channel individually. This specific noise estimator has been shown to estimate noise power robustly, and it can track non-stationary noises with reasonably low mean estimation error and low estimation error variance [2].…”

Section: Noise Power Estimationmentioning

confidence: 99%

“…Traditional speech enhancement algorithms often work only in narrowly specified conditions or with specific noise statistics. Algorithms exist and work well, when the background noise is stationary and non-speech [2][3][4][5], however, these algorithms often fail when competing speakers are present. A possible solution to this problem is to use source separation algorithm like beamform-ing [6,7].…”

Section: Introductionmentioning

confidence: 99%

Speech Enhancement via Combination of Wiener Filter and Blind Source Separation

Taghia

Sang

et al. 2011

Advances in Intelligent and Soft Computing

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Abstract. Automatic speech recognition (ASR) often fails in acoustically noisy environments. Aimed to improve speech recognition scores of an ASR in a real-life like acoustical environment, a speech pre-processing system is proposed in this paper, which consists of several stages: First, a convolutive blind source separation (BSS) is applied to the spectrogram of the signals that are pre-processed by binaural Wiener filtering (BWF). Secondly, the target speech is detected by an ASR system recognition rate based on a Hidden Markov Model (HMM). To evaluate the performance of the proposed algorithm, the signal-to-interference ratio (SIR), the improvement signal-to-noise ratio (ISNR) and the speech recognition rates of the output signals were calculated using the signal corpus of the CHiME database. The results show an improvement in SIR and ISNR, but no obvious improvement of speech recognition scores. Improvements for future research are suggested.

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An evaluation of noise power spectral density estimation algorithms in adverse acoustic environments

Cited by 47 publications

References 8 publications

Unbiased MMSE-Based Noise Power Estimation With Low Complexity and Low Tracking Delay

Unbiased MMSE-Based Noise Power Estimation With Low Complexity and Low Tracking Delay

Estimating acoustic speech features in low signal-to-noise ratios using a statistical framework

Speech Enhancement via Combination of Wiener Filter and Blind Source Separation

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