Comparison of Various Algorithms: Research on Piano Audio Signal Feature Identification

Authors

Keywords:

piano, audio features, convolutional neural network, dynamic time warping

Abstract

Accurate identification of piano audio signals helps in piano learning and composition. This article briefly introduced feature extraction methods for piano audio signals and three algorithms, dynamic time warping (DTW), back-propagation (BP), and convolutional neural network (CNN), which can recognize piano audio features. The three recognition algorithms were compared in the subsequent simulation experiments. It was found that for some single-note and multi-note piano audios, the recognition results of the CNN algorithm were consistent with the standard results, the BP algorithm had some differences, and the DTW algorithm had the most differences. As the number of notes in the piano audio increased, the recognition accuracy of all the algorithms decreased, but the CNN algorithm decreased the least, and its recognition performance was highest under the same number of notes, followed by the BP algorithm, and the DTW algorithm was the lowest.

Additional Files

Published

2023-08-24

How to Cite

1.
Hao S. Comparison of Various Algorithms: Research on Piano Audio Signal Feature Identification. Canadian Acoustics [Internet]. 2023 Aug. 24 [cited 2024 Oct. 6];51(2). Available from: https://jcaa.caa-aca.ca/index.php/jcaa/article/view/3941

Issue

Vol. 51 No. 2 (2023)

Section

Article - Signal Processing / Numerical Methods

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