Audio sound event identification for distress situations and context awareness.
In this paper the acoustic event detection and classification (AED/AEC) system developed under European Community's Seventh Framework Companionable project in awareness context is presented. The system relies on the use of Wavelet transform technique for detection and on an unsupervised order estimation of Gaussian mixture model (GMM) arranged in hierarchical form in the aim to improve the recognition accuracy. The results, measured in terms of two metrics (accuracy and error rate) are obtained applying the implemented system in off-line mode of audio analysis form of distress scenarios recorded in this fact.
Rougui
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JE
LRIT-ESIGETEL, Rue du Port de Valvins, 77210 Avon, France.
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Souidene
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D000162:Acoustics; D000465:Algorithms; D001185:Artificial Intelligence; D001364:Awareness; D003198:Computer Simulation; D006801:Humans; D016011:Normal Distribution; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D017603:Signal Detection, Psychological; D012815:Signal Processing, Computer-Assisted; D013016:Sound; D013018:Sound Spectrography / Q000379:methods; D014732:Vibration