Skip to main content
SpringerLink
Log in

Temporal Acoustic Words for Online Acoustic Event Detection

  • Conference paper
  • First Online:
Pattern Recognition (DAGM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9358))

Included in the following conference series:

Abstract

The Bag-of-Features principle proved successful in many pattern recognition tasks ranging from document analysis and image classification to gesture recognition and even forensic applications. Lately these methods emerged in the field of acoustic event detection and showed very promising results. The detection and classification of acoustic events is an important task for many practical applications like video understanding, surveillance or speech enhancement. In this paper a novel approach for online acoustic event detection is presented that builds on top of the Bag-of-Features principle. Features are calculated for all frames in a given window. Applying the concept of feature augmentation additional temporal information is encoded in each feature vector. These feature vectors are then softly quantized so that a Bag-of-Feature representation is computed. These representations are evaluated by a classifier in a sliding window approach. The experiments on a challenging indoor dataset of acoustic events will show that the proposed method yields state-of-the-art results compared to other online event detection methods. Furthermore, it will be shown that the temporal feature augmentation significantly improves the recognition rates.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    A video of the proposed method applied in our lab can be found at: https://vimeo.com/134489154 .

References

  1. Aucouturier, J.J., Defreville, B., Pachet, F.: The Bag-of-Frames Approach to Audio Pattern Recognition: A Sufficient Model for Urban Soundscapes but Not for Polyphonic Music. J. Acoust. Soc. Am. 122(2), 881–891 (2007)

    Article  Google Scholar 

  2. Carletti, V., Foggia, P., Percannella, G., Saggese, A., Strisciuglio, N., Vento, M.: Audio Surveillance using a Bag of Aural Words Classifier. In: 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 81–86. IEEE (2013)

    Google Scholar 

  3. Chatfield, K., Lempitsky, V., Vedaldi, A., Zisserman, A.: The devil is in the details: an evaluation of recent feature encoding methods. In: Proceeding British Machine Vision Conference (BMVC) (2011)

    Google Scholar 

  4. Fink, G.A.: Markov Models for Pattern Recognition. From Theory to Applications. Advances in Computer Vision and Pattern Recognition, 2nd edn. Springer, London (2014)

    Book  Google Scholar 

  5. Foggia, P., Saggese, A., Strisciuglio, N., Vento, M.: Cascade classifiers trained on Gammatonegrams for reliably detecting Audio Events. In: 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 50–55. IEEE (2014)

    Google Scholar 

  6. Giannoulis, D., Benetos, E., Stowell, D., Rossignol, M., Lagrange, M., Plumbley, M.D.: Detection and classification of acoustic scenes and events: an IEEE AASP challenge. In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp. 1–4. IEEE (2013)

    Google Scholar 

  7. Good, P.: Permutation Tests - A Practical Guide to Resampling Methods for Testing Hypotheses. Springer Series in Statistics, 2nd edn. Springer, New York (2000)

    Google Scholar 

  8. Grzeszick, R., Rothacker, L., Fink, G.A.: Bag-of-Features Representations using Spatial Visual Vocabularies for Object Classification. In: Proceeding International Conference on Image Processing (ICIP) (2013)

    Google Scholar 

  9. Jiang, Y.G., Bhattacharya, S., Chang, S.F., Shah, M.: High-level event recognition in unconstrained videos. Int. J. Multimedia Inf. Retrieval 2(2), 73–101 (2013)

    Article  Google Scholar 

  10. Klinck, H., Stelzer, K., Jafarmadar, K., Mellinger, D.K.: AAS Endurance: An Autonomous Acoustic Sailboat for Marine Mammal Research. In: International Robotic Sailing Conference (2009)

    Google Scholar 

  11. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proceeding IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 2169–2178 (2006)

    Google Scholar 

  12. Nogueira, W., Roma, G., Herrera, P.: Automatic Event Classification using Front End Single Channel Noise Reduction, MFCC Features and a Support Vector Machine Classifier. Technical report, IEEE AASP Challenge: Detection and Classification of Acoustic Scenes and Events (2013). http://c4dm.eecs.qmul.ac.uk/sceneseventschallenge/abstracts/OL/NR2.pdf

  13. Pancoast, S., Akbacak, M.: Bag-of-audio-words approach for multimedia event classification. In: Interspeech, pp. 2105–2108 (2012)

    Google Scholar 

  14. Phan, H., Maasz, M., Mazur, R., Mertins, A.: Random regression forests for acoustic event detection and classification. IEEE/ACM Trans. Audio Speech Lang. Process. 23(1), 20–31 (2014). http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6949625

    Article  Google Scholar 

  15. Phan, H., Mertins, A.: Exploiting superframe cooccurence for acoustic event recognition. In: European Signal Processing Conference (2014)

    Google Scholar 

  16. Plinge, A., Grzeszick, R., Fink, G.A.: A bag-of-features approach to acoustic event detection. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (2014)

    Google Scholar 

  17. Sánchez, J., Perronnin, F., De Campos, T.: Modeling the spatial layout of images beyond spatial pyramids. Pattern Recogn. Lett. 33(16), 2216–2223 (2012)

    Article  Google Scholar 

  18. Schröder, J., Cauchi, B., Schädler, M.R., Moritz, N., Adiloglu, K., Anemüller, J., Doclo, S., Kollmeier, B., Goetze, S.: Acoustic event detection using signal enhancement and spectro-temporal feature extraction. Technical report, IEEE AASP Challenge: Detection and Classification of Acoustic Scenes and Events (2013). http://c4dm.eecs.qmul.ac.uk/sceneseventschallenge/abstracts/OL/SCS.pdf

  19. Shao, Y., Srinivasan, S., Wang, D.: Incorporating auditory feature uncertainties in robust speaker identification. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 277–280 (2007)

    Google Scholar 

  20. Shivappa, S.T., Trivedi, M.M., Rao, B.D.: Audiovisual information fusion in human computer interfaces and intelligent environments: a survey. Proc. IEEE 98(10), 1692–1715 (2010)

    Article  Google Scholar 

  21. Steele, D., Krijnders, J.D., Guastavino, C.: The Sensor City Initiative: Cognitive Sensors for Soundscape Transformations. GIS Ostrava (2013)

    Google Scholar 

  22. Tang, H., Chu, S.M., Hasegawa-Johnson, M., Huang, T.S.: Partially supervised speaker clustering. IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 959–971 (2012)

    Article  Google Scholar 

  23. Temko, A., Malkin, R.G., Zieger, C., Macho, D., Nadeu, C., Omologo, M.: CLEAR evaluation of acoustic event detection and classification systems. In: Stiefelhagen, R., Garofolo, J.S. (eds.) CLEAR 2006. LNCS, vol. 4122, pp. 311–322. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  24. Vuegen, L., Broeck, B.V.D., Karsmakers, P., Gemmeke, J.F., Vanrumste, B., Hamme, H.V.: An MFCC-GMM approach for event detection and classification. Technical report, IEEE AASP Challenge: Detection and Classification of Acoustic Scenes and Events (2013). http://c4dm.eecs.qmul.ac.uk/sceneseventschallenge/abstracts/OL/VVK.pdf

  25. Wang, D., Brown, G.J. (eds.): Computational Auditory Scene Analysis: Principles, Algorithms, and Applications. IEEE Press (2006)

    Google Scholar 

  26. Young, S.H., Scanlon, M.V.: Robotic vehicle uses acoustic array for detection and localization in Urban environments. in: SPIE Proceeding Mobile Robot Perception, vol. 4364, pp. 264–273 (2001)

    Google Scholar 

  27. Zeppelzauer, M., Stöger, A.S., Breiteneder, C.: Acoustic detection of elephant presence in noisy environments. In: Proceedings of the 2nd ACM international workshop on Multimedia analysis for ecological data, pp. 3–8. ACM (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, TU Dortmund, Dortmund, Germany

    Rene Grzeszick, Axel Plinge & Gernot A. Fink

Authors
  1. Rene Grzeszick
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Axel Plinge
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gernot A. Fink
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rene Grzeszick .

Editor information

Editors and Affiliations

  1. Institute of Computer Science III, University of Bonn, Bonn, Germany

    Juergen Gall

  2. MPI for Intelligent Systems, University of Tübingen, Tübingen, Germany

    Peter Gehler

  3. Computer Vision Group, Visual Computing Institute, RWTH Aachen, Aachen, Germany

    Bastian Leibe

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Grzeszick, R., Plinge, A., Fink, G.A. (2015). Temporal Acoustic Words for Online Acoustic Event Detection. In: Gall, J., Gehler, P., Leibe, B. (eds) Pattern Recognition. DAGM 2015. Lecture Notes in Computer Science(), vol 9358. Springer, Cham. https://doi.org/10.1007/978-3-319-24947-6_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-24947-6_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24946-9

  • Online ISBN: 978-3-319-24947-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation