Security Monitoring Using Microphone Arrays and Audio Classification

Abu-El-Quran, A.R.; Goubran, Rafik; Chan, Adrian D. C.

doi:10.1109/tim.2006.876394

Cited by 45 publications

(23 citation statements)

References 15 publications

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“…A speech/nonspeech classifier is used as a frontend to appropriately select the next stage in audio processing, which is either the SI system or the nonspeech audio classification system [9]. The speech/nonspeech classifier is also adapted to the environment, so that it is robust to varying levels of noise.…”

Section: Instrument Overviewmentioning

confidence: 99%

“…In [9,14], pitch ratio (PR) was used to perform speech/nonspeech classification. The PR algorithm calculates the presence of human pitch (70 Hz < human pitch < 280 Hz) in multiple small frames in certain audio segment duration as shown in Figure 3.…”

Section: Speech/nonspeechmentioning

confidence: 99%

“…This PR value is then compared to a certain threshold. The speech/nonspeech decision is made as follows: In [9], a threshold was empirically determined as a function of frame size (FS), segment duration (SD), and overlap (OL). Noise was shown to deteriorate the pitch period of sound.…”

Section: Speech/nonspeechmentioning

confidence: 99%

“…We have found that the optimal threshold (Figure 4(a)), the threshold corresponding to the maximum speech/nonspeech classification accuracy (Figure 4(b)), reduces as the SNR reduces. This adaptive speech/nonspeech algorithm was termed the adaptive pitch ratio (APR) algorithm [9]. Adaption is performed based on the SNR estimates from the DiscreteValued SNR Estimator.…”

Section: Speech/nonspeechmentioning

confidence: 99%

“…This security instrument is capable of robustly determining the identity and location of a talker, even in a noisy environment. The instrument is also capable of identifying the audio type and location of nonspeech audio signals (e.g., footsteps, windows breaking, and cocktail noise) [9]. This instrument can be incorporated in a variety of applications, such as handsfree audio conferencing systems to perform voice-based security authentication for the conference users [10].…”

Section: Introductionmentioning

confidence: 99%

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Multiengine Speech Processing Using SNR Estimator in Variable Noisy Environments

Abu-El-Quran¹,

Chan

Goubran

2012

Advances in Acoustics and Vibration

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We introduce a multiengine speech processing system that can detect the location and the type of audio signal in variable noisy environments. This system detects the location of the audio source using a microphone array; the system examines the audio first, determines if it is speech/nonspeech, then estimates the value of the signal to noise (SNR) using a Discrete-Valued SNR Estimator. Using this SNR value, instead of trying to adapt the speech signal to the speech processing system, we adapt the speech processing system to the surrounding environment of the captured speech signal. In this paper, we introduced the Discrete-Valued SNR Estimator and a multiengine classifier, using Multiengine Selection or Multiengine Weighted Fusion. Also we use the SI as example of the speech processing. The Discrete-Valued SNR Estimator achieves an accuracy of 98.4% in characterizing the environment's SNR. Compared to a conventional single engine SI system, the improvement in accuracy was as high as 9.0% and 10.0% for the Multiengine Selection and Multiengine Weighted Fusion, respectively.

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Section: Instrument Overviewmentioning

confidence: 99%

Section: Speech/nonspeechmentioning

confidence: 99%

Section: Speech/nonspeechmentioning

confidence: 99%

Section: Speech/nonspeechmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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