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TUT Acoustic Event Detection System 2007

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4625))

Abstract

This paper describes a system used in acoustic event detection task of the CLEAR 2007 evaluation. The objective of the task is to detect acoustic events (door slam, steps, paper wrapping etc.) using acoustic data from a multiple microphone set up in the meeting room environment. A system based on hidden Markov models and multi-channel audio data was implemented. Mel-Frequency Cepstral Coefficients are used to represent the power spectrum of the acoustic signal. Fully-connected three-state hidden Markov models are trained for 12 acoustic events and one-state models are trained for speech, silence, and unknown events.

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References

  1. Temko, A., Malkin, R., 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)

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  2. Temko, A., Nadeu, C.: Classification of acoustic events using SVM-based clustering schemes. Pattern Recognition 39(4), 682–694 (2006)

    Article  MATH  Google Scholar 

  3. Gaunard, P., Mubikangiey, C., Couvreur, C., Fontaine, V.: Automatic Classification of Environmental Noise Events by Hidden Markov Models. Applied Acoustics 54(3), 187–206 (1998)

    Article  Google Scholar 

  4. Eronen, A., Tuomi, J., Klapuri, A., Fagerlund, S., Sorsa, T., Lorho, G., Huopaniemi, J.: Audio-based context recognition. IEEE Transactions on Audio, Speech, and Language Processing 14(1), 321–329 (2006)

    Article  Google Scholar 

  5. Rabiner, L., Juang, B.H.: Fundamentals of Speech Recognition. PTR Prentice-Hall Inc, New Jersey (1993)

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  6. CLEAR: AED Evaluation Plan (2007), http://isl.ira.uka.de/clear07/download=CLEAR_2007_AED_EvaluationPlan.pdf

  7. NIST: Spring (RT-05S) Rich Transcription Meeting Recognition Evaluation Plan (2005), http://nist.gov/speech/tests/rt/rt2005/spring/rt05smeetingeval-plan-V1.pdf

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Rainer Stiefelhagen Rachel Bowers Jonathan Fiscus

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© 2008 Springer-Verlag Berlin Heidelberg

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Heittola, T., Klapuri, A. (2008). TUT Acoustic Event Detection System 2007. In: Stiefelhagen, R., Bowers, R., Fiscus, J. (eds) Multimodal Technologies for Perception of Humans. RT CLEAR 2007 2007. Lecture Notes in Computer Science, vol 4625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68585-2_35

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  • DOI: https://doi.org/10.1007/978-3-540-68585-2_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68584-5

  • Online ISBN: 978-3-540-68585-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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