Harmonic Classification with Enhancing Music Using Deep Learning Techniques

Wen Tang, Linlin Gu and Dan Selistean

Complexity, 2021, vol. 2021, 1-10

Abstract: Automatic extraction of features from harmonic information of music audio is considered in this paper. Automatically obtaining of relevant information is necessary not just for analysis but also for the commercial issue such as music program of tutoring and generating of lead sheet. Two aspects of harmony are considered, chord and global key, facing the issue of the extraction problem by the algorithm of machine learning. Contribution here is to recognize chords in the music by the feature extraction method (voiced models) that performd better than manually one. The modelling carried out chord sequence, getting from frame-by-frame basis, which is known in recognition of the chord system. Technique of machine learning such the convolutional neural network (CNN) will systematically extract the chord sequence to achieve the superiority context model. Then, traditional classification is used to create the key classifier which is better than others or manually one. Datasets used to evaluate the proposed model show good achievement results compared with existing one.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/complexity/2021/5590996.pdf (application/pdf)
http://downloads.hindawi.com/journals/complexity/2021/5590996.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:5590996

DOI: 10.1155/2021/5590996

Access Statistics for this article

More articles in Complexity from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().