Non-linear semantic embedding for organizing large instrument sample libraries

Eric J. Humphrey, Aron P. Glennon, Juan Pablo Bello

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Though tags and metadata may provide rich indicators of relationships between high-level concepts like songs, artists or even genres, verbal descriptors lack the fine-grained detail necessary to capture acoustic nuances necessary for efficient retrieval of sounds in extremely large sample libraries. To these ends, we present a flexible approach titled Non-linear Semantic Embedding (NLSE), capable of projecting high-dimensional time-frequency representations of musical instrument samples into a low-dimensional, semantically-organized metric space. As opposed to other dimensionality reduction techniques, NLSE incorporates extrinsic semantic information in learning a projection, automatically learns salient acoustic features, and generates an intuitively meaningful output space.

Original languageEnglish (US)
Title of host publicationProceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011
Pages142-147
Number of pages6
DOIs
StatePublished - 2011
Event10th International Conference on Machine Learning and Applications, ICMLA 2011 - Honolulu, HI, United States
Duration: Dec 18 2011Dec 21 2011

Publication series

NameProceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011
Volume2

Other

Other10th International Conference on Machine Learning and Applications, ICMLA 2011
CountryUnited States
CityHonolulu, HI
Period12/18/1112/21/11

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction

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  • Cite this

    Humphrey, E. J., Glennon, A. P., & Bello, J. P. (2011). Non-linear semantic embedding for organizing large instrument sample libraries. In Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011 (pp. 142-147). [6147663] (Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011; Vol. 2). https://doi.org/10.1109/ICMLA.2011.105