Navneet Upadhyay

The spectral subtractive-type algorithms for enhancing speech in noisy environments

The spectral subtraction is a traditional approach for enhancing the quality of speech degraded by environmental noise. This algorithm is based on the subtraction of the estimated noise spectrum from the noisy speech spectrum and combines it with the phase of the noisy speech. Besides suppressing the noise, this method introduces an unnatural and unpleasant remnant noise. Several variants of this algorithm have been adapted over the years to address its shortcomings, mainly the quality of the remnant noise and its trade-off with speech distortion. This paper presents a comparative performance analysis of different spectral subtractive-type single channel speech enhancement algorithms and experimental results show that the modified forms of spectral subtraction efficiently reduce the remnant noise and improve signal-to-noise ratio.

Single channel speech enhancement utilizing iterative processing of multi-band spectral subtraction algorithm

The spectral subtraction method is a conventional approach for single channel speech enhancement. The basic principle of this method is to estimate the short-time spectral magnitude of speech by subtracting estimated noise from the noisy speech spectrum and to combines it with the phase of the noisy speech. Besides reducing the noise, this method generates an unnatural and unpleasant noise, called remnant noise. This paper proposes a novel algorithm to reduce the remnant noise, and thus improving the overall quality of the enhanced speech. In this algorithm, the output of multi-band spectral subtraction (MBSS) method is used as the input signal again for next iteration process. After the MBSS method, the additive noise is changed to remnant noise. The remnant noise is re-estimated at each iteration. The new estimated noise, furthermore, is been used to process the next MBSS. This procedure is iterated a small number of times. The simulation results as well as informal subjective evaluations prove that the speech enhanced by proposed algorithm is more pleasant to listeners than the conventional MBSS algorithm. This reveals that the proposed algorithm reduces remnant noise satisfactorily and produces good speech quality with improved signal-to-noise ratio.

A perceptually motivated stationary wavelet packet filterbank using improved spectral over-subtraction for enhancement of speech in various noise environments

In this paper, we propose a speech enhancement method where the front-end decomposition of the input speech is performed by temporally processing using a filterbank. The proposed method incorporates a perceptually motivated stationary wavelet packet filterbank (PM-SWPFB) and an improved spectral over-subtraction (I-SOS) algorithm for the enhancement of speech in various noise environments. The stationary wavelet packet transform (SWPT) is a shift invariant transform. The PM-SWPFB is obtained by selecting the stationary wavelet packet tree in such a manner that it matches closely the non-linear resolution of the critical band structure of the psychoacoustic model. After the decomposition of the input speech, the I-SOS algorithm is applied in each subband, separately for the estimation of speech. The I-SOS uses a continuous noise estimation approach and estimate noise power from each subband without the need of explicit speech silence detection. The subband noise power is estimated and updated by adaptively smoothing the noisy signal power. The smoothing parameter in each subband is controlled by a function of the estimated signal-to-noise ratio (SNR). The performance of the proposed speech enhancement method is tested on speech signals degraded by various real-world noises. Using objective speech quality measures (SNR, segmental SNR (SegSNR), perceptual evaluation of speech quality (PESQ) score), and spectrograms with informal listening tests, we show that the proposed speech enhancement method outperforms than the spectral subtractive-type algorithms and improves quality and intelligibility of the enhanced speech.

Channel estimation for OFDM systems using Kalman filter algorithm

This paper presents the Kalman filter (KF) based channel estimation algorithm for orthogonal frequency division multiplexing (OFDM) systems. The cyclic prefix (CP) portion of the OFDM symbols is used for extracting the channel state information. The KF algorithm computes a channel estimate based on the information contained in the cyclic prefix. This channel estimation algorithm is compared with the classical least squares (LS) estimation approach. The simulation result shows that the KF algorithm outperforms then LS method. Absence of pilot signals and better adaptability to channel variations are other advantages of the KF method.

A Perceptually Motivated Multi-Band Spectral Subtraction Algorithm for Enhancement of Degraded Speech

The spectral subtraction method is a classical approach for enhancement of degraded speech. The basic principle of this technique is to estimate the short-time spectral magnitude of speech by subtracting estimated noise from the noisy speech spectrum and to combine it with the phase of the noisy speech. Besides reducing the noise, this method generates an unnatural and unpleasant noise, called remnant noise. The other drawback of this method is that it can work only for white Gaussian noise which has a flat spectrum and is distributed uniformly over the frequency spectrum. But real-world noise is mostly colored and has a non-uniform spectrum. To take care of this kind of noises, spectral subtraction algorithm has been extended to a multi-band case with uniformly spaced frequency bands. In this paper, a perceptually motivated multi-band spectral subtraction algorithm is proposed to enhance the speech signal degraded by colored noise. In the proposed scheme, the whole speech spectrum is divided in different non-uniform bands (N = 6) in accordance to the critical-band rate scale and spectral subtraction is executed independently in each band. The simulation results as well as informal subjective evaluations show that the proposed algorithm reduces remnant noise efficiently and the enhanced speech contains minimal speech distortions with improved signal-to-noise ratio.

An auditory perception based improved multi-band spectral subtraction algorithm for enhancement of speech degraded by non-stationary noises

In this paper, an auditory perception based improved multi-band spectral subtraction algorithm is proposed to enhance the speech signal degraded by non-stationary or colored noises. In the proposed scheme, the whole speech spectrum is divided in different non-uniform bands (N = 6) in accordance to the critical-band rate scale and spectral subtraction is applied separately in each band. The proposed algorithm uses a new approach to estimate the noise power from each band without the need of explicit speech silence detection. The noise estimate is updated by adaptively smoothing the noisy signal power. The smoothing parameter is controlled by a linear function of a-posteriori signal-to-noise ratio (SNR). This noise estimation approach gives accurate results at low SNR and works continuously in the presence of speech. The objective measures as well as informal subjective tests demonstrate that the proposed algorithm reduces remnant noise efficiently and the enhanced speech contains minimal speech distortions with improved SNR.

A perceptually motivated stationary wavelet packet filter-bank utilizing improved spectral over-subtraction algorithm for enhancing speech in non-stationary environments

This paper proposes a novel speech enhancement approach for a single-microphone system to meet the demand of quality noise reduction algorithms. The proposed system incorporates a perceptually motivated stationary wavelet packet filter-bank (PM-SWPFB) and improved spectral over-subtraction (I-SOS) algorithm together to enhance the speech degraded by non-stationary or colored noise environment. The PM-SWPFB is obtained by adjusting the uniformly spaced stationary wavelet packet tree in order to most closely mimic the critical-bands of the psycho-acoustic model. The PM-SWPFB is, firstly, used to decompose the input noisy speech signal into nonuniform sub-bands. Then, I-SOS algorithm is used to estimate of speech from each sub-band. The I-SOS algorithm uses a new noise estimation approach, to estimate noise power from each sub-band without the need of explicit speech silence detection. The sub-band noise estimate is updated by adaptively smoothing the noisy signal power. The smoothing parameter is controlled by a function of the estimated signal-to-noise ratio (SNR). The performance of the proposed speech enhancement system is evaluated objectively by SNR, Itakura-Saito distortion measure and subjectively by informal listening test. The results confirm that the proposed speech enhancement system is capable of reducing noise with little speech degradation remains acceptable in real-world environments, and the overall performance is superior to several competitive methods.