Robust Voice Activity Detection Using the Spectral Peaks of Vowel Sounds

Yoo, In-Chul; Yook, Dongsuk

doi:10.4218/etrij.09.0209.0104

Cited by 30 publications

(26 citation statements)

References 5 publications

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“…(b) A new proposed VAD algorithm that is based on a binary weighting of the spectral components of the signal under test [1]. This algorithm, explained in the section 2 B, is based on the property that vowels have distinctive spectral peaks.…”

Section: Introductionmentioning

confidence: 99%

“…This one is spectral subtraction method in which we have developed our own mathematical formula for the peak valley detection (PVD) of the frequency spectra to detect the voice activity [1]. The purpose of this research is to compare the performance of this SNR based PVD (SNRPVD) method over Zero-Crossing rate detector [5] and statistical analysis based algorithms [10].…”

Section: Voice Activity Detection (Vad) Problem Considers Detecting Tmentioning

confidence: 99%

“…Using this concept we propose the Signal to noise Ratio peak valley difference (SNRPVD) which calculates the similarity between the peak signature vector S of a registered vowel sound and the spectrum X of the input signal. In this by using one conventional existing peak valley detection formula [1] and applying it on several of our data sets and for our application we have concluded and modified the formula to this following formula.…”

Section: B Peak Valley Detection Algorithm For the Voice Activity Dementioning

confidence: 99%

See 2 more Smart Citations

An Efficient Peak Valley Detection Based VAD Algorithm for Robust Detection of Speech Auditory Brainstem Responses

Narayanam¹

2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Voice Activity Detection (VAD) problem considers detecting the presence of speech in a noisy [7,8,9]. This one is spectral subtraction method in which we have developed our own mathematical formula for the peak valley detection (PVD) of the frequency spectra to detect the voice activity [1]. The purpose of this research is to compare the performance of this SNR based PVD (SNRPVD) method over Zero-Crossing rate detector [5] and statistical analysis based algorithms [10]. We have put into application of these three algorithms on these particular data sets of this experiment [7,8,9] and VAD is verified and compared the results of these three. MATLAB routines were developed on these particular methodologies. Finally we concluded that the method of SNRPVD surely performing better than the ZCR and statistical algorithms.

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Section: Introductionmentioning

confidence: 99%

Section: Voice Activity Detection (Vad) Problem Considers Detecting Tmentioning

confidence: 99%

Section: B Peak Valley Detection Algorithm For the Voice Activity Dementioning

confidence: 99%

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An Efficient Peak Valley Detection Based VAD Algorithm for Robust Detection of Speech Auditory Brainstem Responses

Narayanam¹

2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…Speech Detector is based on the PeakValley-Difference (PVD) parameter. The feature used is a modification of the parameter proposed by Yoo and Yook [45] and often used in Voice Activity Detection (VAD) algorithms. The PVD is calculated as follows:…”

Section: Sound Event Detectionmentioning

confidence: 99%

Detection, classification and localization of acoustic events in the presence of background noise for acoustic surveillance of hazardous situations

Lopatka

Kotus

Czyżewski

2015

Multimed Tools Appl

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Evaluation of sound event detection, classification and localization of hazardous acoustic events in the presence of background noise of different types and changing intensities is presented. The methods for discerning between the events being in focus and the acoustic background are introduced. The classifier, based on a Support Vector Machine algorithm, is described. The set of features and samples used for the training of the classifier are introduced. The sound source localization algorithm based on the analysis of multichannel signals from the Acoustic Vector Sensor is presented. The methods are evaluated in an experiment conducted in the anechoic chamber, in which the representative events are played together with noise of differing intensity. The results of detection, classification and localization accuracy with respect to the Signal to Noise Ratio are discussed. The results show that the recognition and localization accuracy are strongly dependent on the acoustic conditions. We also found that the engineered algorithms provide a sufficient robustness in moderately intense noise in order to be applied to practical audio-visual surveillance systems.

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“…Therefore, the key to detect speech sounds is to find the repetitive peaks in the power spectrum. The parameter known as peak-valley difference parameter (PVD) is used for voice activity detectors (VAD), which are part of speech processing or recognition systems [16]. The parameter is based on the difference between spectral peaks and valleys of vowels spectral representation,.…”

Section: Detection Of Speech Signalsmentioning

confidence: 99%

Detection and localization of selected acoustic events in acoustic field for smart surveillance applications

Kotus

Lopatka

Czyżewski

2012

Multimed Tools Appl

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A method for automatic determination of position of chosen sound events such as speech signals and impulse sounds in 3-dimensional space is presented. The events are localized in the presence of sound reflections employing acoustic vector sensors. Human voice and impulsive sounds are detected using adaptive detectors based on modified peak-valley difference (PVD) parameter and sound pressure level. Localization based on signals from the multichannel acoustic vector probe is performed upon the detection. The described algorithms can be employed in surveillance systems to monitor behavior of public events participants. The results can be used to detect sound source position in real time or to calculate the spatial distribution of sound energy in the environment. Moreover, the spatial filtration can be performed to separate sounds arriving from a chosen direction.

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Robust Voice Activity Detection Using the Spectral Peaks of Vowel Sounds

Cited by 30 publications

References 5 publications

An Efficient Peak Valley Detection Based VAD Algorithm for Robust Detection of Speech Auditory Brainstem Responses

An Efficient Peak Valley Detection Based VAD Algorithm for Robust Detection of Speech Auditory Brainstem Responses

Detection, classification and localization of acoustic events in the presence of background noise for acoustic surveillance of hazardous situations

Detection and localization of selected acoustic events in acoustic field for smart surveillance applications

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