4.5 Article

Tensorial dynamic time warping with articulation index representation for efficient audio-template learning

期刊

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA
卷 143, 期 3, 页码 1548-1558

出版社

ACOUSTICAL SOC AMER AMER INST PHYSICS
DOI: 10.1121/1.5027245

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资金

  1. TerraSwarm Research Center
  2. STARnet phase of the Focus Center Research Program (FCRP), a Semiconductor Research Corporation program - MARCO
  3. 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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