A Real-Time Sound Recognition System in an Assisted Environment
Conference paper
Abstract
This article focuses on the development of detection and classification system of environmental sounds in real-time in a typical home for persons with disabilities. Based on the extraction of acoustic characteristics (Mel Frequency Cepstral Coefficients, Zero Crossing Rate, Roll Off Point and Spectral Centroid) and using a probabilistic classifier (Gaussian Mixture Model), preliminary results show an accuracy rate greater than 93% in the detection and 98% in the classification task
Keywords
Gaussian Mixture Model Acoustic Characteristic Zero Crossing Rate Spectral Centroid Audio Classification
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
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