A New Approach for Voice Spectrum Analysis (Vsa): Suitable for Pervasive Computing Using Matlab

Chaudhari, Vijay K; Singh, Rakesh K.; Varshney, Dinesh; Kumar, Shiv

doi:10.1142/9789814289986_0074

Cited by 3 publications

(14 citation statements)

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“…Earlier Chaudhari and coworkers [1], proposed an approach for VSA and the analysis of the results were not discussed. The present study is an extension of our earlier work [1] for High Pitch, Low Pitch, FIIR, and IIR is included with flow chart, and DFD.…”

Section: Problem Identificationmentioning

confidence: 99%

“…The present study is an extension of our earlier work [1] for High Pitch, Low Pitch, FIIR, and IIR is included with flow chart, and DFD. We may add that Jeeawoody [3] does not considered VSA for N bits long parameters for security devices.…”

Section: Problem Identificationmentioning

confidence: 99%

“…Recorded voice signal is then converted to the frequency domain through the use of a shifted FFT and correctly scaled frequency vector [1].…”

Section: A Speech Enhancementmentioning

confidence: 99%

“…The average pitch of the entire wav file was computed and plotted as shown in figure. The results of pitch analysis can be used in voice recognition, where the differences in average pitch can be used to characterize a voice file. Pitch defines two parameters [1]:…”

Section: B Pitch Analysismentioning

confidence: 99%

“…Earlier, Chaudhari and coworkers [1] and Shiv Kumar and coworkers [2] develops a method for voice signal compression for limited bits, N bit signal is used and also the signal is compressed upto 50% of the original signal with out changing the extension of the compressed signal as illustrated in table-III [2]. We may add that the work of Jeeawoody [3] that is based on limited bits of signal with limited parameters.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Analysis of Voice Signal Based On High Pitch, Low Pitch, FIIR and IIR Spectrum Suitable For Pervasive Computing Using MatLab

Chaudhari¹,

Kumar²,

Singh³

et al. 2010

IJCEE

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Section: Problem Identificationmentioning

confidence: 99%

Section: Problem Identificationmentioning

confidence: 99%

“…Recorded voice signal is then converted to the frequency domain through the use of a shifted FFT and correctly scaled frequency vector [1].…”

Section: A Speech Enhancementmentioning

confidence: 99%

Section: B Pitch Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of Voice Signal Based On High Pitch, Low Pitch, FIIR and IIR Spectrum Suitable For Pervasive Computing Using MatLab

Chaudhari¹,

Kumar²,

Singh³

et al. 2010

IJCEE

Self Cite

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Voice Signal Compression and Spectrum Analysis (VSCSA): Suitable For Pervasive Computing and Limited Storage Devices Using MatLab

Chaudhari

Srivastava²,

Singh

et al. 2009

2009 IEEE International Advance Computing Conference

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In this paper, Voice Signal Compression and These masking models are called Psychoacoustic Models Spectrum Analysis (VSCSA) is a technique that is used to (PM). The original PM is defined in Figure-[l].compress transparent high quality voice signals upto 45% -Psychoacoustic model is taken by reference no.- [3]. PM 60% of the source file at low bit rate 45kbps with same calculates a masking threshold, which is the maximum extension (i.e. .wav to .wav), then voice spectrum analysis distortion energy masked by the signal energy from each (VSA) is started. Voice Signal Compression (VSC) is done coding portion developed by us. The psychoacoustic model is by using adaptive wavelet packet a tool of MatLab for based on many studies of human perception. Effects due to

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An Approach for Automatic Voice Signal Detection (AVSD) Using Matlab

Theory¹,

Kumar²,

Shastri³

2011

IJCTE

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Abstract-In this paper titled: "Automatic Voice Signal Detection (AVSD)", is a technique which identify constitutional and unconstitutional voice signal automatically. AVSD is performed on the basis of frequency content of voice signal for several samples of the same sound. An underline purpose of this paper is to investigate automatically fake voice signal in the security system. Frequency content is mapped to the frequency domain by computing its DFT using the FFT algorithm. The voice signal detection algorithm is formulated by continually computing the difference of the absolute average of two adjacent voice windows, and comparing it to a predefined threshold.Index Terms-Algorithm, Frequency Content, Threshold, FFT, Window I. INTRODUCTIONHuman beings can here the frequency range approximately from 20Hz to 20 KHz. If a frequency that is very close to 20Hz to 20 KHz, could assume that for hearing this closed signal becomes more difficult. Human auditory systems have a perceptual property that may be auditory threshold of hearing (ATH) or auditory masking [5][6][7][8][9]. When a strong signal makes a temporal or spectral neighborhood of weaker signal minute, this means that masking effect can be observed in the time and frequency domain. If two signals occur at the same time and one signal is masked by another signal than this is called simultaneous masking or frequency masking. A weak signal emitted after the end of louder signal and masked by louder signal.By different news channel and different Media we generally come to know that a person is denying from the statement which may or may not be given by him, by saying that this is not his voice. We thought for above problem and finally decided to develop an approach so that we will be able to find whether the person is innocent or not. AVSD is an approach to find-out whether a person is denying from his/her statement or not mean distinguish between the constitutional and unconstitutional voice signal.Voice signal detection is becoming increasingly popular and can be found in luxury cars, mobile phones, dictation office software, toys, and appliances. It is defined as the Prof. Aditya Shastri is Vice Chancellor of Banasthali University, Tonk (Rajasthan)-304022, India (Phone: +91-1438-228373, 228787, E-mail: adityashastri@yahoo.com).Prof. R.K. Singh is Vice Chancellor of ITM University, Gurgaon (Haryana), India (Phone: +91-124-2365811, 2365812, E-mail: vcrks@rediffmail.com).ability to identify a spoken word or a sequence of words. A. Wavelet TheoryThe fundamental idea behind wavelets is to analyze according to the scale. Indeed, some researchers in the wavelet field feel that, by using wavelets, one is adopting a whole new mindset or perspective in processing data. Wavelets are functions which satisfy certain mathematical requirements and are used in representing data or other functions. Approximation using superposition of functions has existed since the arly 1800's, when Joseph Fourier discovered that he could superpose sines and cosines to represent other func...

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A New Approach for Voice Spectrum Analysis (Vsa): Suitable for Pervasive Computing Using Matlab

Cited by 3 publications

References 0 publications

Analysis of Voice Signal Based On High Pitch, Low Pitch, FIIR and IIR Spectrum Suitable For Pervasive Computing Using MatLab

Analysis of Voice Signal Based On High Pitch, Low Pitch, FIIR and IIR Spectrum Suitable For Pervasive Computing Using MatLab

Voice Signal Compression and Spectrum Analysis (VSCSA): Suitable For Pervasive Computing and Limited Storage Devices Using MatLab

An Approach for Automatic Voice Signal Detection (AVSD) Using Matlab

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