Environmental Background Sounds Classification Based on Properties of Feature Contours

  • Tomasz Maka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7906)

Abstract

In this paper, an approach to environmental sound recognition (ESR) by using properties of feature trajectories is presented. To determine the discriminative attributes of background sounds, several audio classes have been analysed. Selected groups of sounds reflect the acoustical environments that may occur in real sound acquisition situations. We proposed the feature extraction scheme, where obtained trajectories at parameterization stage are further processed in order to improve classification accuracy. A discriminatory analysis of popular audio features for ESR task has been performed. Obtained results show that proposed technique gives promising classification results and can be applied in systems where properly identified audio scene can improve other audio processing tasks.

Keywords

environmental sounds recognition ESR feature contours audio classification 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Al-Zhrani, S., AlQahtani, M.: Audio Environment Recognition using Zero Crossing Features and MPEG-7 Descriptors. Journal of Computer Science 6(11), 1283–1287 (2010)CrossRefGoogle Scholar
  2. 2.
    Chang, C., Lin, C.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
  3. 3.
    Chu, S., Narayanan, S., Jay Kuo, C.-C.: Content analysis for acoustic environment classification in mobile robots. In: Proceedings of the AAAI Fall Symposium, Aurally Informed Performance: Integrating Machine Listening and Auditory Presentation in Robotic Systems, Arlington, Va, USA (2006)Google Scholar
  4. 4.
    Feki, I., Ammar, A., Alimi, A.: Audio stream analysis for environmental sound classification. In: Proceedings of the International Conference on Multimedia Computing and Systems (ICMCS) (2011)Google Scholar
  5. 5.
    Ganchev, T.: Contemporary Methods for Speech Parameterization. Springer, New York (2011)CrossRefGoogle Scholar
  6. 6.
    Ghoraani, B., Krishnan, S.: Time-Frequency Matrix Feature Extraction and Classification of Environmental Audio Signals. IEEE Transactions on Audio, Speech and Language Processing 19(7), 2197–2209 (2011)CrossRefGoogle Scholar
  7. 7.
    Hall, M.: Correlation-based Feature Subset Selection for Machine Learning. Hamilton, New Zealand (1998)Google Scholar
  8. 8.
    Han, B., Hwang, E.: Environmental sound classification based on feature collaboration. In: Proceedings of the 2009 IEEE International Conference on Multimedia and Expo (ICME), New York (2009)Google Scholar
  9. 9.
    Maka, T.: Features of Average Spectral Envelope for Audio Regions Determination. In: International Conference on Signals and Electronic Systems, ICSES 2012, Wroclaw, Poland, September 19-21 (2012)Google Scholar
  10. 10.
    Mitrovic, D., Zeppelzauer, M., Breiteneder, C.: Features for Content-Based Audio Retrieval. In: Advances in Computers Improving the Web, vol. 78, pp. 71–150 (2010)Google Scholar
  11. 11.
    Press, W., Teukolsky, S., Vetterling, W., Flannery, B.: Numerical Recipes: The Art of Scientific Computing. Cambridge University Press (2007)Google Scholar
  12. 12.
    Peeters, G.: A large set of audio features for sound description (similarity and classification) in the CUIDADO project, CUIDADO I.S.T. Project Report (2004)Google Scholar
  13. 13.
    Rabiner, L., Schafer, W.: Theory and Applications of Digital Speech Processing. Prentice-Hall (2010)Google Scholar
  14. 14.
    Rodemann, T., Joublin, F., Goerick, C.: Filtering environmental sounds using basic audio cues in robot audition. In: Proceedings of International Conference on Advanced Robotics (ICAR), Munich, Germany. IEEE-RAS (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tomasz Maka
    • 1
  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of Technology, SzczecinSzczecinPoland

Personalised recommendations