Environmental Non-Speech Sound Recognition Using Hidden Markov Model. Case Study: Glass Break Sounds

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This paper introduces the basics of environmental or non-speech sound recognition by presenting data-preprocessing, feature extraction and classification techniques used in sound recognition systems. A new method easily implemented on embedded digital signal processors, based on Hidden Markov Models HMM and applied to glass break sounds is presented. Data preprocessing includes analog to digital converting, sampling and anti-aliasing filtering. Features extraction includes zero-crossing, envelope function detecting, spectral mean and bank filtering. Classification technique includes vector quantization and HMM. The method's MATLABTM functions are shown. Tests show 88% recognition rate and 7% false alarms.
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Sound Recognition; Signal Processing; Hidden Markov Model

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