• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.865-868
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• 2000

In this paper we derive a new VAD algorithm, which combines the preprocessing algorithm and the optimum decision rule. To improve the performance of the VAD algorithm we employ the speech enhancement algorithm and then apply the maximal ratio combining technique in the preprocessing procedure, which leads to maximized output SNR. Moreover, we also perform extensive computer simulations to demonstrate the performance improvement of the proposed algorithm under various background noise environments.

• Journal of Broadcast Engineering
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• v.17 no.3
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• pp.519-528
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• 2012

Voice activity detection (VAD) is generally conducted by extracting features from the acoustic signal and a decision rule. The performance of such VAD algorithms driven by the input acoustic signal highly depends on the acoustic noise. When video signals are available as well, the performance of VAD can be enhanced by using the visual information which is not affected by the acoustic noise. Previous visual VAD algorithms usually use single visual feature to detect the lip activity, such as active appearance models, optical flow or intensity variation. Based on the analysis of the weakness of each feature, we propose to combine intensity change measure and the optical flow in the mouth region, which can compensate for each other's weakness. In order to minimize the computational complexity, we develop simple measures that avoid statistical estimation or modeling. Specifically, the optical flow is the averaged motion vector of some grid regions and the intensity variation is detected by simple thresholding. To extract the mouth region, we propose a simple algorithm which first detects two eyes and uses the profile of intensity to detect the center of mouth. Experiments show that the proposed combination of two simple measures show higher detection rates for the given false positive rate than the methods that use a single feature.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.2211-2217
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• 2009

In this paper, we propose the speech enhancement algorithm using an one-microphone in a reverberant room environments. Spectral subtraction is the effective method which can reduce the reverberation element and the noise in a spectrum domain. Spectral subtraction needs correct separation of voice section and silent section therefore to improve the performance, voice activity detection(VAD) based on entropy has been applied to the proposed method. We test a performance of the proposed method by comparing with conventional method which used VAD based on energy detection. Reverberation reduction ratio with variable of SNR and a reverberation time is used as a test index. From the simulation result, proposed method shows performance better than conventional method.

• The Journal of the Acoustical Society of Korea
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• v.36 no.5
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• pp.345-352
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• 2017

We propose a methods which is reducing direction estimation error of sound source in the reverberant and noisy environments. The proposed algorithm divides speech signal into voice and unvoice using VAD. We estimate the direction of source when current frame is voiced. TDOA (Time-Difference of Arrival) between microphone array using the GCC-PHAT (Generalized Cross Correlation with Phase Transform) method will be estimated in that frame. Then, we compare the peak value of cross-correlation of two signals applied to estimated time-delay with other time-delay in time-table in order to improve the accuracy of source location. If the angle of current frame is far different from before and after frame in successive voiced frame, the angle of current frame is replaced with mean value of the estimated angle in before and after frames.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11C
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• pp.1161-1168
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• 2003

In this paper, we propose an improved statistical model-based voice activity detection algorithm and threshold update method. We first improve signal-to-noise ratio by using speech enhancement preprocessing algorithm combined power subtraction method and matched filter, then apply it to LLR test optimum decision rule for improving the performance even in low SNR conditions. And we propose an adaptive threshold update method that was not concerned in any papers. We also perform extensive computer simulations to demonstrate the performance improvement of the proposed VAD algorithm employing the proposed speech enhancement preprocessing algorithm and adaptive threshold update method under various background noise environments. Finally we verify our results by comparing ITU-T G.729 Annex B.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.8
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• pp.851-858
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• 2016

This paper presents a new statistical voice activity detection (VAD) based on the probabilistic interpretation of nonnegative matrix factorization (NMF). The objective function of the NMF using Kullback-Leibler divergence coincides with the negative log likelihood function of the data if the distribution of the data given the basis and encoding matrices is modeled as Poisson distributions. Based on this probabilistic NMF, the VAD is constructed using the likelihood ratio test assuming that speech and noise follow Poisson distributions. Experimental results show that the proposed approach outperformed the conventional Gaussian model-based and NMF-based methods at 0-15 dB signal-to-noise ratio simulation conditions.

• The Journal of the Acoustical Society of Korea
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• v.22 no.4
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• pp.286-292
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• 2003

Voice activity detection (VAD) is a process that separates the noise region from silence or noise region of input speech signal. Since unvoiced consonant signals have very similar characteristics to those of noise signals, it may result in serious distortion of unvoiced consonants, or in erroneous noise estimation to can out VAD without paying special attention on unvoiced consonants. In this paper, we propose a method to extract in an explicit way the boundaries between unvoiced consonant and noise in fluent speech so that more exact VAD could be performed. The proposed method is based on histogram in frequency domain which was successfully used by Hirsch for noise estimation, and a1so on similarity measure of frequency components between adjacent frames, To evaluate the performance of the proposed method, experiments on unvoiced consonant boundary extraction was performed on seven kinds of noisy speech signals of 10 ㏈ and 15 ㏈ SNR respectively.

• International Journal of Control, Automation, and Systems
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• v.5 no.1
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• pp.61-69
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• 2007

In this paper, we developed not only a reliable sound localization system including a VAD(Voice Activity Detection) component using three microphones but also a face tracking system using a vision camera. Moreover, we proposed a way to integrate three systems in the human-robot interaction to compensate errors in the localization of a speaker and to reject unnecessary speech or noise signals entering from undesired directions effectively. For the purpose of verifying our system's performances, we installed the proposed audio-visual system in a prototype robot, called IROBAA(Intelligent ROBot for Active Audition), and demonstrated how to integrate the audio-visual system.

• The Journal of the Acoustical Society of Korea
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• v.28 no.4
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• pp.401-407
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• 2009

In this paper, we propose a novel method for the cross-correlation based double-talk detection (DTD), which employing the Gaussian Mixture Model (GMM) in the frequency domain. The proposed algorithm transforms the cross correlation coefficient used in the time domain into 16 channels in the frequency domain using the discrete fourier transform (DFT). The channels are then selected into seven feature vectors for GMM and we identify three different regions such as far-end, double-talk and near-end speech using the likelihood comparison based on those feature vectors. The presented DTD algorithm detects efficiently the double-talk regions without Voice Activity Detector which has been used in conventional cross correlation based double-talk detection. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional schemes. especially, show the robustness against detection errors resulting from the background noises or echo path change which one of the key issues in practical DTD.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.12
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• pp.1744-1749
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• 2013

Acoustic feedback is perceived as whistling or howling, which is a major complaint of hearing-aids users. Acoustic feedback cancellation is important in hearing-aids because acoustic feedback degrades performance of the hearing aid device by reducing maximum insertion gain. Adaptive systems for estimate acoustic feedback path and feedback suppression algorithms have been proposed in order to solve this problem. A typical feedback cancellation algorithm is LMS(least mean squares) because of its computational efficiency. However it has problem of convergence performance in high correlated input signal. In this paper, we propose a new variable step-size normalized LMS(least mean squares) algorithm using VAD(voice activity detection) to overcome the limitation of the LMS algorithm. The VAD algorithm is GSAP(global speech absence probability) and the feedback cancellation algorithm is normalized LMS. The proposed algorithm applies different step-size between voice and non-voice using VAD, for high stability, fast convergence speed and low misalignment when correlated inputs, such as speech. The result of simulation with white noise mixed speech signal, the proposed algorithm shows high performance then traditional algorithm in terms of stability, convergence speed and misalignment.