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

Long N. Le, Douglas L. Jones

Research output: Contribution to journalArticle


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.

Original languageEnglish (US)
Pages (from-to)1548-1558
Number of pages11
JournalJournal of the Acoustical Society of America
Issue number3
StatePublished - Mar 1 2018


ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Acoustics and Ultrasonics

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