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
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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