Automatic music genre recognition for in-car infotainment

Automatic music genre recognition is fundamental tool for music retrieval, recommendation and personalisation in smart infotainment systems and music streaming services. Such systems may be helpful especially for in-car audio, because driver’s interaction with such infotainment systems could become a major subject of his/her distraction. There are two important tasks to be considered for better genre classification, which present classifier and audio features extraction. In the proposed system, timbral textural and pitch content features were used for genre classification. Timbral texture includes the Mel-Frequency Cepstral Coefficients (MFCC) along with other spectral characteristics. For the pitch content the features extracted from Chroma are selected. The aim of this work is to explore possibilities of music genres classification from audio signal and to create a system for automatic recognition of music genres in the MATLAB programming environment. A functional system for recognition of music genres was developed on the GTZAN data set with ten different musical genres such as rock, pop, classical etc. The authors examined several classification methods including GMM, SVM, and k-NN. The experimental results show that by using both types of features, the classification accuracy of 69.7% is achieved for the k-NN classifier.

• Record URL:
  https://doi.org/10.1016/j.trpro.2019.07.189
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S2352146519303588
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23521465
• Supplemental Notes:
  • © 2019 Maros Jakubec and Michal Chmulik. Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Jakubec, Maros
  • Chmulik, Michal
• Conference:
  • TRANSCOM 2019 the 13th International Scientific Conference on Sustainable, Modern and Safe Transport
  • Location: Novy Smokovec High Tatras, Slovakia
  • Date: 2019-5-29 to 2019-5-31
• Publication Date: 2019

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 1364-1371
• Serial:
  • Transportation Research Procedia
  • Volume: 40
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2352-1465
  • Serial URL: http://www.sciencedirect.com/science/journal/23521465/
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Music; Radio equipment
• Uncontrolled Terms: Automatic music genre recognition; In-vehicle technologies; Infotainment; Intelligent driver vehicle interfaces
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01715745
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 2 2019 4:33PM