Properties Of Human Auditory System Research Articles

Speech enhancement based on perceptually motivated guided spectrogram filtering

In this paper, a single-channel speech enhancement algorithm is proposed by using guided spectrogram filtering based on masking properties of human auditory system when considering a speech spectrogram as an image. Guided filtering is capable of sharpening details and estimating unwanted textures or background noise from the noisy speech spectrogram. If we consider the noisy spectrogram as a degraded image, we can estimate the spectrogram of the clean speech signal using guided filtering after subtracting noise components. Combined with masking properties of human auditory system, the proposed algorithm adaptively adjusts and reduces the residual noise of the enhanced speech spectrogram according to the corresponding masking threshold. Because the filtering output is a local linear transform of the guidance spectrogram, the local mask window slides can be efficiently implemented via box filter with O(N) computational complexity. Experimental results show that the proposed algorithm can effectively suppress noise in different noisy environments and thus can greatly improve speech quality and speech intelligibility.

Perceptual Subspace Speech Enhancement with Variance Normalization

In this paper a perceptual subspace speech enhancement method using masking property of human auditory system with variance normalization is presented. The masking property of the human auditory system is used while deciding the gain parameters for the algorithm. Spectral Domain Constrained estimator was employed in determining the filter coefficients and colored noise was handled by replacing the noise variance by Rayleigh quotient. Variance normalization is further done to remove the spikes in the values so as to avoid abrupt increase or decrease in power of the output samples making the output more intelligible. The objective measures SNRLoss and SNRLESC were chosen for performance evaluation based on their efficiency in determining the intelligibility of the output. The results show an improved performance of the proposed method over some of the existing speech enhancement methods in terms of intelligibility.

An Improved Speech Enhancement Algorithm Based on Wiener-Filtering

This paper proposes an improved speech enhancement algorithm based on Wiener-Filtering, which addresses the problems of speech distortion and musical noise. The proposed algorithm adopts the masking properties of human auditory system on calculating the gain of spectrum point, in order that the signal in the enhanced speech whose energy is lower than the threshold will not be decreased further and the less distortion will be brought to enhanced speech by the trade-off between the noise elimination and speech signal distortion. What’s more, in order to eliminate the “musical noise”, a spectrum-shaping technology using averaging method between adjacent frames is adopted. And to guarantee the real-time application, two-stage moving-average strategy is adopted. The computer simulation results show that the proposed algorithm is superior to the traditional Wiener method in the low CPU cost, real-time statistics, the reduction of the speech distortion and residual musical noise.

Speech enhancement using generalized weighted β-order spectral amplitude estimator

In this paper, a single-channel speech enhancement method based on generalized weighted β-order spectral amplitude estimator is proposed. First, we derive a new kind of generalized weighted β-order Bayesian spectral amplitude estimator, which takes full advantage of both the traditional perceptually weighted estimators and β-order spectral amplitude estimators and can obtain flexible and effective gain function. Second, according to the masking properties of human auditory system, the adaptive estimation methods for the perceptually weighted order p is proposed, which is based on a criterion that inaudible noise may be masked rather than removed. Thereby, the distortion of enhanced speech is reduced. Third, based on the compressive nonlinearity of the cochlea, the spectral amplitude order β can be interpreted as the compression rate of the spectral amplitude, and then the adaptive calculation method of parameter β is proposed. In addition, due to one frame delay, the a priori SNR estimation of decision-directed method in speech activity periods is inaccurate. In order to overcome the drawback, we present a new a priori SNR estimation method by combining predicted estimation with decision-directed rule. The subjective and objective test results indicate that the proposed Bayesian spectral amplitude estimator combined with the proposed a priori SNR estimation method can achieve a more significant segmental SNR improvement, a lower log-spectral distortion and a better speech quality over the reference methods.

Improved Spectral Subtraction Speech Enhancement Algorithm

The paper addresses the problems of speech distortion and residual musical noise introduced by conventional spectral subtraction (SS) method for speech enhancement. In this paper, we propose a modified SS algorithm for speech enhancement based on the masking properties of human auditory system. The algorithm computes the parameters α and β dynamically according to the masking thresholds of the critical frequency segments for each speech frame. Simulation results show that the proposed algorithm is superior to the conventional SS method, not only in the improvement of output SNR, but in the reduction of the speech distortion and residual musical noise.

Perceptual speech enhancement exploiting temporal masking properties of human auditory system

The use of simultaneous masking in speech enhancement has shown promise for a range of noise types. In this paper, a new speech enhancement algorithm based on a short-term temporal masking threshold to noise ratio (MNR) is presented. A novel functional model for forward masking based on three parameters is incorporated into a speech enhancement framework based on speech boosting. The performance of the speech enhancement algorithm using the proposed forward masking model was compared with seven other speech enhancement methods over 12 different noise types and four SNRs. Objective evaluation using PESQ revealed that using the proposed forward masking model, the speech enhancement algorithm outperforms the other algorithms by 6–20% depending on the SNR. Moreover, subjective evaluation using 16 listeners confirmed the objective test results.

On Line Secret Watermark Generation for Audio Files

This paper presents an on line dynamic robust and efficient approach of audio watermarking for copyright protection. In this paper I am using on line secret watermark generation to generate additional information (watermark) automatically during embedding process. The Direct Sequence Spread Spectrum (DSSS) technique is taken to spread the watermark bits over the entire spectrum of audio signals imperceptibly by creating chip sequences using the bits of watermark. The watermark is shaped in audio signals without the violation of psycho acoustic properties of Human Auditory System (HAS) in frequency domain. The generated watermark is unique for every audio file. The size of watermark depends upon the size and numbers of audio file samples.

A Perceptually Constrained GSVD-Based Approach for Enhancing Speech Corrupted by Colored Noise

The singular value decomposition (SVD)-based method for single-channel speech enhancement has been shown to be very useful when the additive noise is white. For colored noise, with this approach, one needs to whiten the noise spectrum prior to SVD-based approach and perform the inverse whitening processing afterwards. A truncated quotient SVD (QSVD)-based approach has been proposed to handle this problem and found very useful. In this paper, a generalized SVD (GSVD)-based subspace approach for speech enhancement is first extended from the concept of the truncated QSVD-based approach, in which the dimension of the signal subspace can be precisely and automatically determined for each frame of the noisy signal. But with this new approach some residual noise is still perceivable under lower signal-to-noise ratio conditions. Therefore a perceptually constrained GSVD (PCGSVD)-based approach is further proposed to incorporate the masking properties of human auditory system to make sure the undesired residual noise to be nearly un-perceivable. Closed-form solutions are obtained for both the GSVD- and PCGSVD-based enhancement approaches. Very carefully performed objective evaluations and subjective listening tests show that the PCGSVD-based approach proposed here can offer improved speech quality, intelligibility and recognition accuracy, whether the noise is stationary or nonstationary, especially when the additive noise is nonwhite

Wavelet-Based Speech Enhancement Using Time-Frequency Adaptation

Wavelet denoising is commonly used for speech enhancement because of the simplicity of its implementation. However, the conventional methods generate the presence of musical residual noise while thresholding the background noise. The unvoiced components of speech are often eliminated from this method. In this paper, a novel algorithm of wavelet coefficient threshold (WCT) based on time-frequency adaptation is proposed. In addition, an unvoiced speech enhancement algorithm is also integrated into the system to improve the intelligibility of speech. The wavelet coefficient threshold (WCT) of each subband is first temporally adjusted according to the value of a posterior signal-to-noise ratio (SNR). To prevent the degradation of unvoiced sounds during noise, the algorithm utilizes a simple speech/noise detector (SND) and further divides speech signal into unvoiced and voiced sounds. Then, we apply appropriate wavelet thresholding according to voiced/unvoiced (V/U) decision. Based on the masking properties of human auditory system, a perceptual gain factor is adopted into wavelet thresholding for suppressing musical residual noise. Simulation results show that the proposed method is capable of reducing noise with little speech degradation and the overall performance is superior to several competitive methods.

Enhanced Itakura measure incorporating masking properties of human auditory system

A new enhanced Itakura (E-Itakura) speech distortion measure is proposed in this paper. It incorporates masking properties of the human auditory system into the original Itakura measure. Inaudible noise components masked by speech signals are excluded from the calculation of the E-Itakura measure, while the intrinsic advantage of the Itakura measure is retained. The proposed new measure has been compared with the original Itakura distortion, frequency-weighted Itakura spectral distortion, cepstral distance and Bark spectral distortion measures. The comparison results show that the correlation between the original Itakura measure with speech quality has been improved from 0.73 to 0.89 with the incorporation of the enhancement feature, and that the E-Itakura measure offers a more consistent indication of the subjective quality of speech.