In this paper, we propose a new speech enhancement system using wavelet thresholding algorithm. In the proposed system, we introduce a new algorithm which does not require the voiced/unvoiced detection process. Also an adaptive threshold value and a new thresholding function are introduced to improve the speech enhancement performances and speech auditory quality. A voice activity detector (VAD...

This paper proposes a voice activity detector (VAD) based on the complex Laplacian model. With the use of a goodness-of-fit (GOF) test, it is discovered that the Laplacian model is more suitable to describe noisy speech distribution than the conventional Gaussian model. The likelihood ratio (LR) based on the Laplacian model is computed and then applied to the VAD operation. According to the exp...

This paper proposes a novel voice activity detector (VAD) based on singular value decomposition (SVD). The spectro-temporal characteristics of background noise region can be easily analyzed by SVD. The proposed method naturally drops hangover algorithm from VAD. Moreover, it adaptively changes the decision threshold by employing the most dominant singular value of the observation matrix in the ...

This paper deals with Kalman filter-based enhancement of a speech signal contaminated by a white noise, using a single microphone system. Such a problem can be stated as a realization issue in the framework of identification. For such a purpose we propose to identify the state space model by using subspace non-iterative algorithms based on orthogonal projections. Unlike Estimate-Maximize (EM)-b...

The task of robustly detecting distant speech in low SNR environments for automatic speech recognition is examined using a two-stage approach based on two distinguishing features of speech, namely harmonicity and modulation frequency (MF). A modified metric for harmonicity is used as a gating function to a set of parallel classifiers that incorporate MFs computed on different frequency bands. P...

On speech signal processing, it is very important to find the fundamental frequency of voice. The reason is why it is used in variable places, such as speech-enhancement-system, speech-recognition system, speaker-classification-system, and handicapped assisting-system. However the pitch detection is difficult when the original signal is corrupted by noise, or put in transition section of voice....

Robustness against background noise is a major research area for speech-related applications such as speech recognition and speaker recognition. One of the many solutions for this problem is to detect speech-dominant regions by using a voice activity detector (VAD). In this paper, a second-order polynomial regression-based algorithm is proposed with a similar function as a VAD for text-independ...

Abstract—A simple adaptive voice activity detector (VAD) is implemented using Gabor and gammatone atomic decomposition of speech for high Gaussian noise environments. Matching pursuit is used for atomic decomposition, and is shown to achieve optimal speech detection capability at high data compression rates for low signal to noise ratios. The most active dictionary elements found by matching pu...

Nowadays, several computational techniques for speech recognition have been proposed. These techniques suppose an important improvement in real time applications where speaker interacts with speech recognition systems. Although researchers proposed many methods, none of them solve the high false alarm problem when far-field speakers interfere in a human-machine conversation. This paper presents...

Acoustic echo cancellation is one of the oldest applications of the adaptive filters and today part of each speakerphone. An important block of each acoustic echo canceller is the double talk detector. It blocks the adaptation of the filter when near end voice is present and thus preventing the adaptive filter from diverging from the optimal position. In this paper we present an improved versio...