Automatic music genre classification based on musical instrument track separation

作者：Rosner Aldona^*; Kostek Bozena

来源：Journal of Intelligent Information Systems, 2018, 50(2): 363-384.

DOI：10.1007/s10844-017-0464-5

• Music information retrieval (MIR)
• Automatic music genre classification
• Automatic separation of music tracks
• Support vector machine (SVM)

The aim of this article is to investigate whether separating music tracks at the pre-processing phase and extending feature vector by parameters related to the specific musical instruments that are characteristic for the given musical genre allow for efficient automatic musical genre classification in case of database containing thousands of music excerpts and a dozen of genres. Results of extensive experiments show that the approach proposed for music genre classification is promising. Overall, conglomerating parameters derived from both an original audio and a mixture of separated tracks improve classification effectiveness measures, demonstrating that the proposed feature vector and the Support Vector Machine (SVM) with Co-training mechanism are applicable to a large dataset.

• 出版日期2018-4