Audio Signal Classification Using Support Vector Machines

  • Lei-Ting Chen
  • Ming-Jen Wang
  • Chia-Jiu Wang
  • Heng-Ming Tai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


As the internet community grows larger, digital music distribution becomes widely available and is made easier than ever. Artists from all over the world can make their songs available by a single click. Websites, containing varieties of music style for download, charge only a fraction of the cost of a CD for the service. With the incredible amount of music pieces available, it is impossible to classify each piece by its style manually. A procedure is proposed using the support vector statistical learning algorithm to achieve the task autonomously. Digital music files are converted, partitioned and processed to obtain the desirable input vectors for the algorithm. As the machine learns the features of each music genre, it is capable of classifying input vectors from unknown pieces. A simulation was carried out to evaluate the efficiency of the algorithm. Results from the simulation are presented and discussed in this paper. Conclusions are drawn by comparing other algorithms against the proposed method.


Support Vector Machine Hide Markov Model Music Piece Linear Predictive Code Magnitude Frequency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar
  2. Burges, C.: A Tutorial on Support Vector Machines for Pattern Recognition,
  3. Moore, A.: Support Vector Machines Tutorial. Carnegie Mellon University,
  4. Cover, T.M.: Geometrical and Statistical Properties of Systems of Linear Inequality with Applications in Pattern Recognition. IEEE Trans. Electronic Computers 14, 326–334 (1965)zbMATHCrossRefGoogle Scholar
  5. Chai, W., Vercoe, B.: Folk Music Classification Using Hidden Markov Models. Technical Report, MIT,
  6. Easy MP3 Wav Converter,
  7. Tzanetakis, G., Essl, G., Cook, P.: Automatic Musical Genre Classification of Audio Signals. Computer Science and Music Department, Princeton University,
  8. Scott, P.: Music Classification Using Neural Networks,
  9. Deshpande, H., Singh, R., Nam, U.: Classification of Music Signals in the Visual Domain. In: Proceedings of the COST G-6 Conference on Digital Audio Effects (Page DAFX-01), Limerick, Ireland, December 6-8 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lei-Ting Chen
    • 1
  • Ming-Jen Wang
    • 2
  • Chia-Jiu Wang
    • 2
  • Heng-Ming Tai
    • 3
  1. 1.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.Department of Electrical and Computer EngineeringUniversity of Colorado at ColoradoSpringsUSA
  3. 3.Department of Electrical EngineeringUniversity of TulsaTulsaUSA

Personalised recommendations