Voiced/unvoiced speech discrimination in noise using Gabor atomic decomposition

Lobo, Arthur P.; Loizou, Philipos C.

doi:10.1109/icassp.2003.1198907

Cited by 49 publications

(18 citation statements)

References 13 publications

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“…Here we can see that in most of the cases, clean speech has accuracy more than 96% and this value will increase if we take sample length longer enough. The Average accuracy of the proposed method is approximately 92% which is quite high in case of noisy speech signal compare to other methods [14], [15]. Most of the errors occurred in voiced to unvoiced or unvoiced to voiced transaction frames.…”

Section: Experiments Details and Resultsmentioning

confidence: 75%

A Method for Voiced/Unvoiced Classification of Noisy Speech by Analyzing Time-Domain Features of Spectrogram Image

Hassan¹

2017

CSSP

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This paper presents a voiced/unvoiced classification algorithm of the noisy speech signal by analyzing two acoustic features of the speech signal. Short-time energy and short-time zero-crossing rates are one of the most distinguishable time domain features of a speech signal to classify its voiced activity into voiced/unvoiced segment. A new idea is developed where frame by frame processing has done in narrow band speech signal using spectrogram image. Two time domain features, short-time energy (STE) and short-time zero-crossing rate (ZCR) are used to classify its voiced/unvoiced parts. In the first stage, each frame of the analyzing spectrogram is divided into three separate sub bands and examines their short-time energy ratio pattern. Then an energy ratio pattern matching look up table is used to classify the voicing activity. However, this method successfully classifies patterns 1 through 4 but fails in the rest of the patterns in the look up table. Therefore, the rest of the patterns are confirmed in the second stage where frame wise short-time average zero-crossing rate is compared with a threshold value. In this study, the threshold value is calculated from the short-time average zero-crossing rate of White Gaussian Noise (wGn). The accuracy of the proposed method is evaluated using both male and female speech waveforms under different signal-to-noise ratios (SNRs). Experimental results show that the proposed method achieves better accuracy than the conventional methods in the literature.

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Section: Experiments Details and Resultsmentioning

confidence: 75%

A Method for Voiced/Unvoiced Classification of Noisy Speech by Analyzing Time-Domain Features of Spectrogram Image

Hassan¹

2017

CSSP

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show abstract

“…It has previously been researched in reference [14]. Due to the extremely redundancy of D ¼ ðg γ ðtÞÞ γ A Γ , an appropriate countable subset of atoms ðg γn ðtÞÞ n A N must be selected to represent any functions f(t) with γ n ¼ ðs n ; u n ; v n ; w n Þ, then f(t) can be written as…”

Section: Time-frequency Atomic Decompositions and Matching Pursuitmentioning

confidence: 99%

A novel signal sparse decomposition based on modulation correlation partition

Zhao

Yang

et al. 2016

Neurocomputing

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“…If designed appropriately, parametric dictionaries can yield interpretable information about meaningful signal characteristics for a variety of applications. Previous studies have used Gabor [69] and Gammatone [24] atoms to represent speech signals because of their good localization properties and similarities to the human auditory system. Other efforts have proposed Gaussian-like functions to efficiently capture spherical stereo images [70], diffusion-based dictionaries to model MRI [25], and other wavelet-like atoms for digitizing fingerprint images [71].…”

Section: Choosing the Parametric Dictionary Functionmentioning

confidence: 99%

Markov Chain Monte Carlo Inference of Parametric Dictionaries for Sparse Bayesian Approximations

Chaspari

Tsiartas

Tsilifis

et al. 2016

IEEE Trans. Signal Process.

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Parametric dictionaries can increase the ability of sparse representations to meaningfully capture and interpret the underlying signal information, such as encountered in biomedical problems. Given a mapping function from the atom parameter space to the actual atoms, we propose a sparse Bayesian framework for learning the atom parameters, because of its ability to provide full posterior estimates, take uncertainty into account and generalize on unseen data. Inference is performed with Markov Chain Monte Carlo, that uses block sampling to generate the variables of the Bayesian problem. Since the parameterization of dictionary atoms results in posteriors that cannot be analytically computed, we use a Metropolis-Hastings-within-Gibbs framework, according to which variables with closed-form posteriors are generated with the Gibbs sampler, while the remaining ones with the Metropolis Hastings from appropriate candidate-generating densities. We further show that the corresponding Markov Chain is uniformly ergodic ensuring its convergence to a stationary distribution independently of the initial state. Results on synthetic data and real biomedical signals indicate that our approach offers advantages in terms of signal reconstruction compared to previously proposed Steepest Descent and Equiangular Tight Frame methods. This paper demonstrates the ability of Bayesian learning to generate parametric dictionaries that can reliably represent the exemplar data and provides the foundation towards inferring the entire variable set of the sparse approximation problem for signal denoising, adaptation and other applications.

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Voiced/unvoiced speech discrimination in noise using Gabor atomic decomposition

Cited by 49 publications

References 13 publications

A Method for Voiced/Unvoiced Classification of Noisy Speech by Analyzing Time-Domain Features of Spectrogram Image

A Method for Voiced/Unvoiced Classification of Noisy Speech by Analyzing Time-Domain Features of Spectrogram Image

A novel signal sparse decomposition based on modulation correlation partition

Markov Chain Monte Carlo Inference of Parametric Dictionaries for Sparse Bayesian Approximations

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