Journal
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA
Volume 143, Issue 3, Pages 1548-1558Publisher
ACOUSTICAL SOC AMER AMER INST PHYSICS
DOI: 10.1121/1.5027245
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Funding
- TerraSwarm Research Center
- STARnet phase of the Focus Center Research Program (FCRP), a Semiconductor Research Corporation program - MARCO
- STARnet phase of the Focus Center Research Program (FCRP), a Semiconductor Research Corporation program - DARPA
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Audio classification techniques often depend on the availability of a large labeled training dataset for successful performance. However, in many application domains of audio classification (e.g., wildlife monitoring), obtaining labeled data is still a costly and laborious process. Motivated by this observation, a technique is proposed to efficiently learn a clean template from a few labeled, but likely corrupted (by noise and interferences), data samples. This learning can be done efficiently via tensorial dynamic time warping on the articulation index-based time-frequency representations of audio data. The learned template can then be used in audio classification following the standard template-based approach. Experimental results show that the proposed approach outperforms both (1) the recurrent neural network approach and (2) the state-of-the-art in the template-based approach on a wildlife detection application with few training samples. (C) 2018 Acoustical Society of America.
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