We describe a method to estimate the power spectral density of nonstationary noise when a noisy speech signal is given. The method can be combined with any speech enhancement algorithm which requires a noise power spectral density estimate. In contrast to other methods, our approach does not use a voice activity detector. Instead it tracks spectral minima in each frequency band without any distinction between speech activity and speech pause. By minimizing a conditional mean square estimation error criterion in each time step we derive the optimal smoothing parameter for recursive smoothing of the power spectral density of the noisy speech signal. Based on the optimally smoothed power spectral density estimate and the analysis of the statistics of spectral minima an unbiased noise estimator is developed. The estimator is well suited for real time implementations. Furthermore, to improve the performance in nonstationary noise we introduce a method to speed up the tracking of the spectral minima. Finally, we evaluate the proposed method in the context of speech enhancement and low bit rate speech coding with various noise types.

/pdf/noise-power-spectral-density-estimation-based-on-optimal-2dc3ftbbiq.pdf

Noise power spectral density estimation based on optimal smoothing and minimum statistics

In this letter, we develop a robust voice activity detector (VAD) for the application to variable-rate speech coding. The developed VAD employs the decision-directed parameter estimation method for the likelihood ratio test. In addition, we propose an effective hang-over scheme which considers the previous observations by a first-order Markov process modeling of speech occurrences. According to our simulation results, the proposed VAD shows significantly better performances than the G.729B VAD in low signal-to-noise ratio (SNR) and vehicular noise environments.

A statistical model-based voice activity detection

Noise spectrum estimation is a fundamental component of speech enhancement and speech recognition systems. We present an improved minima controlled recursive averaging (IMCRA) approach, for noise estimation in adverse environments involving nonstationary noise, weak speech components, and low input signal-to-noise ratio (SNR). The noise estimate is obtained by averaging past spectral power values, using a time-varying frequency-dependent smoothing parameter that is adjusted by the signal presence probability. The speech presence probability is controlled by the minima values of a smoothed periodogram. The proposed procedure comprises two iterations of smoothing and minimum tracking. The first iteration provides a rough voice activity detection in each frequency band. Then, smoothing in the second iteration excludes relatively strong speech components, which makes the minimum tracking during speech activity robust. We show that in nonstationary noise environments and under low SNR conditions, the IMCRA approach is very effective. In particular, compared to a competitive method, it obtains a lower estimation error, and when integrated into a speech enhancement system achieves improved speech quality and lower residual noise.

/pdf/noise-spectrum-estimation-in-adverse-environments-improved-k9jxzivyvw.pdf

Noise spectrum estimation in adverse environments: improved minima controlled recursive averaging

Noise spectrum estimation is a fundamental component of speech enhancement and speech recognition systems. In this paper, we present an Improved Minima Con- trolled Recursive Averaging (IMCRA) approach, for noise es- timation in adverse environments involving non-stationary noise, weak speech components, and low input signal-to- noise ratio (SNR). The noise estimate is obtained by av- eraging past spectral power values, using a time-varying frequency-dependent smoothing parameter that is adjusted by the signal presence probability. The speech presence probability is controlled by the minima values of a smoothed periodogram. The proposed procedure comprises two iter- ations of smoothing and minimum tracking. The rst it- eration provides a rough voice activity detection in each frequency band. Then, smoothing in the second iteration excludes relatively strong speech components, which makes the minimum tracking during speech activity robust. We show that in non-stationary noise environments and under low SNR conditions, the IMCRA approach is very eectiv e. In particular, compared to a competitive method, it obtains a lower estimation error, and when integrated into a speech enhancement system achieves improved speech quality and lower residual noise.

/pdf/noise-spectrum-estimation-in-adverse-environments-improved-2p8w9t313q.pdf

Noise Spectrum Estimation in Adverse Environments: Improved Minima Controlled

https://ecs.utdallas.edu/loizou/cimplants/quality_spcom07.pdf

Subjective comparison and evaluation of speech enhancement algorithms

In this paper, a voice activity detector (VAD) for variable rate speech coding is decomposed into two parts, a decision rule and a background noise statistic estimator, which are analysed separately by applying a statistical model. A robust decision rule is derived from the generalized likelihood ratio test by assuming that the noise statistics are known a priori. To estimate the time-varying noise statistics, allowing for the occasional presence of the speech signal, a novel noise spectrum adaptation algorithm using the soft decision information of the proposed decision rule is developed. The algorithm is robust, especially for the time-varying noise such as babble noise.

A voice activity detector employing soft decision based noise spectrum adaptation

We propose a new excitation model for transitional speech to reduce the distortion due to the traditional two-excitation source, voiced unvoiced, model. The proposed low resolution pulse position coding (LRPPC) algorithm detects the existence of pulses at frames of weak periodicity, which are determined as unvoiced, and transmits the approximate pulse positions. In the decoder, dispersed pulses that have a flat magnitude spectrum are synthesized at the decoded positions to form the excitation signal. A subjective quality test shows that the vocoder employing the LRPPC algorithm produces better quality of speech, and is very robust to mode decision errors.

A low resolution pulse position coding method for improved excitation modeling of speech transition

A set of internal wordlengths for implementing an MPEG-2 audio decoder has been determined to minimize the hardware cost while limiting the fixed-point noise to be below the perceptual masking curve. The hardware architecture is based on a data path containing a multiplier and an adder. The finite wordlength effects of three DCT algorithms for the efficient implementation of the subband filtering are also compared. For the bit-accurate fixed-point simulation, the fixed-point optimization utility, which has been developed for the rapid evaluation of finite wordlength effects, is used.

Wordlength optimization of an MPEG-2 audio decoder

At low bit-rates, the error weighting filter based noise shaping technique of the conventional CELP coders often yields audible noise in spectral valley regions due to incomplete noise shaping. In this paper, we propose a simple but effective method to solve the problem. Firstly, we conducted psychoacoustic experiments to show that deterministic noise is more tolerable to the human ear than random noise in spectral valley regions. To exploit this useful auditory property, we apply a spectral shaping filter to the codevectors before the fixed codebook search so as the vector quantizer to be overloaded in spectral valley regions, which makes the quantizer emphasize formant regions while reducing the noise in the spectral valleys. Subjective quality tests show that the proposed method applied to the 5.3 kbps ITU-T G.723.1 CELP coder significantly improves the perceptual quality of the coded speech with little increase of computational cost.

Jongseo Sohn

Papers

A statistical model-based voice activity detection

A voice activity detector employing soft decision based noise spectrum adaptation

A low resolution pulse position coding method for improved excitation modeling of speech transition

Wordlength optimization of an MPEG-2 audio decoder

A codebook shaping method for perceptual quality improvement of CELP coders