11 July 2016 Learning high-level features for chord recognition using Autoencoder
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Proceedings Volume 10011, First International Workshop on Pattern Recognition; 1001117 (2016) https://doi.org/10.1117/12.2242361
Event: First International Workshop on Pattern Recognition, 2016, Tokyo, Japan
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.
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Vilailukkana Phongthongloa, Vilailukkana Phongthongloa, Suwatchai Kamonsantiroj, Suwatchai Kamonsantiroj, Luepol Pipanmaekaporn, Luepol Pipanmaekaporn, } "Learning high-level features for chord recognition using Autoencoder", Proc. SPIE 10011, First International Workshop on Pattern Recognition, 1001117 (11 July 2016); doi: 10.1117/12.2242361; https://doi.org/10.1117/12.2242361
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