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Proceedings Paper

Learning high-level features for chord recognition using Autoencoder
Author(s): Vilailukkana Phongthongloa; Suwatchai Kamonsantiroj; Luepol Pipanmaekaporn
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Paper Abstract

Chord transcription is valuable to do by itself. It is known that the manual transcription of chords is very tiresome, time-consuming. It requires, moreover, musical knowledge. Automatic chord recognition has recently attracted a number of researches in the Music Information Retrieval field. It has known that a pitch class profile (PCP) is the commonly signal representation of musical harmonic analysis. However, the PCP may contain additional non-harmonic noise such as harmonic overtones and transient noise. The problem of non-harmonic might be generating the sound energy in term of frequency more than the actual notes of the respective chord. Autoencoder neural network may be trained to learn a mapping from low level feature to one or more higher-level representation. These high-level representations can explain dependencies of the inputs and reduce the effect of non-harmonic noise. Then these improve features are fed into neural network classifier. The proposed high-level musical features show 80.90% of accuracy. The experimental results have shown that the proposed approach can achieve better performance in comparison with other based method.

Paper Details

Date Published: 11 July 2016
PDF: 5 pages
Proc. SPIE 10011, First International Workshop on Pattern Recognition, 1001117 (11 July 2016); doi: 10.1117/12.2242361
Show Author Affiliations
Vilailukkana Phongthongloa, King Mongkut's Univ. of Technology North Bangkok (Thailand)
Suwatchai Kamonsantiroj, King Mongkut's Univ. of Technology North Bangkok (Thailand)
Luepol Pipanmaekaporn, King Mongkut's Univ. of Technology North Bangkok (Thailand)

Published in SPIE Proceedings Vol. 10011:
First International Workshop on Pattern Recognition
Xudong Jiang; Guojian Chen; Genci Capi; Chiharu Ishll, Editor(s)

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