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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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
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)
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)
Ganchev, T.: Contemporary Methods for Speech Parameterization. Springer, New York (2011)
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)
Hall, M.: Correlation-based Feature Subset Selection for Machine Learning. Hamilton, New Zealand (1998)
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)
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)
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)
Press, W., Teukolsky, S., Vetterling, W., Flannery, B.: Numerical Recipes: The Art of Scientific Computing. Cambridge University Press (2007)
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)
Rabiner, L., Schafer, W.: Theory and Applications of Digital Speech Processing. Prentice-Hall (2010)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Maka, T. (2013). Environmental Background Sounds Classification Based on Properties of Feature Contours. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds) Recent Trends in Applied Artificial Intelligence. IEA/AIE 2013. Lecture Notes in Computer Science(), vol 7906. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38577-3_62
Download citation
DOI: https://doi.org/10.1007/978-3-642-38577-3_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38576-6
Online ISBN: 978-3-642-38577-3
eBook Packages: Computer ScienceComputer Science (R0)