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Adaptive Linear Prediction Filtering in DWT Domain for Real-Time Musical Onset Detection

Abstract

Onset detection is a typical digital signal processing task in acoustic signal analysis, with many applications as in the musical field. Many techniques have been proposed so far, which are typically reliable in terms of performances but often not suitable to real-time computing, for example, they require knowledge of the whole piece to perform optimally, or they are too computationally intensive for most embedded processors. Up to the authors' knowledge, the real-time implementation problem for musical onset detection has been scarcely addressed within the literature, which has motivated them to propose a scalable and computationally efficient algorithm with good detection capabilities. Comparison with other techniques and porting to a real-time embedded processor are discussed as well: provided experimental results seem to confirm the effectiveness of the approach.

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Correspondence to Stefano Squartini.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Gabrielli, L., Piazza, F. & Squartini, S. Adaptive Linear Prediction Filtering in DWT Domain for Real-Time Musical Onset Detection. EURASIP J. Adv. Signal Process. 2011, 650204 (2011). https://doi.org/10.1155/2011/650204

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Keywords

  • Digital Signal Processing
  • Linear Prediction
  • Publisher Note
  • Onset Detection
  • Implementation Problem