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Water sound recognition based on physical models

Abstract : This article describes an audio signal processing algorithm to detect water sounds, built in the context of a larger system aiming to monitor daily activities of elderly people. While previous proposals for water sound recognition relied on classical machine learning and generic audio features to characterize water sounds as a flow texture, we describe here a recognition system based on a physical model of air bubble acoustics. This system is able to recognize a wide variety of water sounds and does not require training. It is validated on a home environmental sound corpus with a classification task, in which all water sounds are correctly detected. In a free detection task on a real life recording, it outperformed the classical systems and obtained 70% of F-measure.
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Submitted on : Monday, May 4, 2015 - 8:55:55 AM
Last modification on : Wednesday, October 14, 2020 - 4:08:04 PM
Long-term archiving on: : Monday, September 14, 2015 - 5:56:13 PM


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  • HAL Id : hal-01147995, version 1
  • OATAO : 12440


Patrice Guyot, Julien Pinquier, Régine André-Obrecht. Water sound recognition based on physical models. IEEE International Conference on Acoustics, Speech, and Signal Processing - ICASSP 2013, May 2013, Vancouver, Canada. pp. 793-797. ⟨hal-01147995⟩



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