Sharif Digital Repository

This paper presents a new approach for hiding information in speech signals. In this method, the silence intervals of speech are found and the length (number of samples) of these intervals is changed to hide information. This method can be used simultaneously with other methods. © 2008 IEEE  

This paper addresses a new approach to segmentation and classification of audio through analysis of a smaller set of selective frames, which are identified by temporal decomposition (TD). These frames are located at the most steady instants, or event centroids, within a given block of the signal, which yield the maximal diversity over the set of selected features. Based on this selection scheme, the number of frames used in the analysis is reduced by at least 40%, while the temporal resolution is doubled as compared to that in typical audio classifiers. By constructing a classification system to segment audio into speech, music, speech-music, and others, it is shown that the proposed method... 

Communication of speech over error prone channels such as wireless channels and internet usually suffers from loss of large number of adjacent samples. In this paper, we propose to make artificial correlation between speech samples which distorts it. By choosing appropriate parameters, one can control this distortion to lie below acceptable ranges. Using this correlation, the receiver can recover lost samples up to a certain limit using our proposed algorithm. Experimental results show that our solution overcomes a previous one reported in the literature specially when the amount of lost samples are below the mentioned limit. ©2007 IEEE  

In the internet telephony, loss of IP packets causes instantaneous discontinuities in the received speech. In this paper, we have focused on finding an error resilient method for this problem. Our proposed method creates artificial correlation between speech samples that pre-distorts the speech signal. The receiver uses this correlation to reconstruct the lost speech packets. An appropriate speech enhancement technique is designed for the reduction of the processing error in the recovered speech caused by the speech codecs. The SegSNR results show the superiority of our proposed speech enhancement method over a recently proposed one. © 2007 IEEE  

In this paper, the performance of the gradient method based on Score Function Difference (SFD) in the separation of i.i.d. and periodic signals will be investigated. We will see that this algorithm will separate periodic signals better than i.i.d. ones. By using this experimental result and the fact that voiced frames of speech signals are approximately periodic, a modified algorithm named VDGaradient has been proposed for separation of speech signals in synthetic convolutive mixtures. In this method, voiced frames of speech signal will be used as the input to the gradient method, then the resulting separating system will be applied to separate sources completely  

In this paper, we have focused on finding an error resilient method for discontinuity-less transmission of speech signals in the internet. Our proposed method creates artificial correlation between speech samples that pre-distorts the speech signal. The receiver uses this correlation to reconstruct the lost speech packets. A discrete Fourier transform (DFT)-based speech enhancement technique is designed for the reduction of the processing error in the recovered speech caused by the speech codecs. The SegSNR results show the superiority of our proposed method over a recently proposed speech enhancement technique. ©2007 IEEE  

In this paper, we suggest to use a steepest descent algorithm for learning a parametric dictionary in which the structure or atom functions are known in advance. The structure of the atoms allows us to find a steepest descent direction of parameters instead of the steepest descent direction of the dictionary itself. We also use a thresholded version of Smoothed- ℓ0 (SL0) algorithm for sparse representation step in our proposed method. Our simulation results show that using atom structure similar to the Gabor functions and learning the parameters of these Gabor-like atoms yield better representations of our noisy speech signal than non parametric dictionary learning methods like K-SVD, in... 

Embedding a secret message into a cover media without attracting any attention, known as steganography, is one of the methods used for hidden communication purposes. One of the cover media that can be used for steganography is speech. In this study, the authors propose a new steganography method in speech signals. In this method, the silence intervals of speech are found and the length (number of samples) of these intervals is changed to hide information. The main feature of our method is robustness to MPEG-1 layer III (MP3) compression. This method can hide information in a speech stream with very low processing time which makes it a real-time steganography method. The hiding capacity of... 

In this paper, a semi-blind multiplicative watermarking approach for audio and speech signals has been presented. At the receiver end, the optimal Maximum Likelihood (ML) detector aided by the channel side information for Gaussian and Laplacian signals in noisy environment is designed and implemented. The performance of the proposed scheme is analytically calculated and verified by simulation. Then, we adapt the proposed scheme to speech and audio signals. To improve robustness, the algorithm is applied to low frequency components of the host signal. Besides, the power of the watermark is controlled elegantly to have inaudibility using Perceptual Evaluation of Audio Quality (PEAQ) and... 

Automatic Speaker Verification (ASV) is authentication of individuals by analyzing their speech signals. Different synthetic approaches allow spoofing to deceive ASV systems (ASVs), whether using techniques to imitate a voice or reconstruct the features. Attackers beat up the ASVs using four general techniques; impersonation, speech synthesis, voice conversion, and replay. The last technique is considered as a common and high potential tool for spoofing purposes since replay attacks are more accessible and require no technical knowledge of adversaries. In this study, we introduce a novel replay spoofing countermeasure for ASVs. Accordingly, we use the Constant Q Cepstral Coefficient (CQCC)... 

In this paper, a sparse component analysis algorithm is presented for the case in which the number of sources is less than or equal to the number of sensors, but the channel (mixing matrix) is time-varying. The method is based on a smoothed l0 norm for the sparsity criteria, and takes advantage of the idea that sparsity of the sources is decreased when they are mixed. The method is able to separate synthetic and speech data, which require very weak sparsity restrictions. It can separate up to 50 mixed signals while being adaptive to channel variation and robust against noise. © 2007 IEEE  

This paper presents a new algorithm for Blind Source Separation (BSS) of Instantaneous speech mixtures in under-determined case. A demixing algorithm which exploits the sparsity of speech signals in the short time Fourier transform (STFT) domain is proposed. This algorithm combines the modified k-means clustering procedure involved in the Line Orientation Separation Technique (LOST) with Smoothed l0-norm minimization (SL0) method. First procedure along with a transformation into a sparse domain tries to estimate the mixing matrix, and the second method tries to extract the sources from the mixtures. Simulation results are presented and compared to the Degenerate Unmixing Estimation Technique... 

Using a proper distribution function for speech signal or for its representations is of crucial importance in statistical-based speech processing algorithms. Although the most commonly used probability density function (pdf) for speech signals is Gaussian, recent studies have shown the superiority of super-Gaussian pdfs. A large research effort has focused on the investigation of a univariate case of speech signal distribution; however, in this paper, we study the multivariate distributions of speech signal and its representations using the conventional distribution functions, e.g., multivariate Gaussian and multivariate Laplace, and the copula-based multivariate distributions as candidates.... 

A major problem in real-time packet-based communication systems, is misrouted or delayed packets which results in degraded perceived voice quality. If packets are not available on time, the packets are considered as lost. The easiest solution in a network terminal receiver is to replace silence for the duration of lost speech segments. In a high quality communication system, to avoid degradation in speech quality due to packet loss, a suitable method or algorithm is needed to replace the missing segments of speech. In this paper, we introduce an adaptive filter for replacement of lost speech segment. In this method Kalman filter as a state-space based method will be used to predict the... 

One of the key constraints of using embedded speech recognition modules is the required computational power. To decrease this requirement, we propose an algorithm that clusters the speech signal before passing it to the recognition units. The algorithm is based on agglomerative clustering and produces a sequence of compressed frames, optimized for recognition. Our experimental results indicate that the proposed method presents a frame rate with average 40 frames per second on medium to large vocabulary isolated word recognition tasks without loss of recognition accuracy which result in up to 60% faster recognition in compare to usual 100 fps fixed frame rate sampling. This value is quite...