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On the use of spectro-temporal features for the IEEE AASP challenge 'detection and classification of acoustic scenes and events'

: Schröder, J.; Moritz, N.; Schädler, M.R.; Cauchi, B.; Adiloglu, K.; Anemüller, J.; Doclo, S.; Kollmeier, B.; Goetze, S.


Institute of Electrical and Electronics Engineers -IEEE-:
IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2013. Proceedings : 20-23 October 2013, New Paltz, NY
Piscataway, NJ: IEEE, 2013
ISBN: 978-1-4799-0972-8
4 pp.
Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) <2013, New Paltz/NY>
Conference Paper
Fraunhofer IDMT ()

In this contribution, an acoustic event detection system based on spectro-temporal features and a two-layer hidden Markov model as back-end is proposed within the framework of the IEEE AASP challenge 'Detection and Classification of Acoustic Scenes and Events' (D-CASE). Noise reduction based on the log-spectral amplitude estimator by [1] and noise power density estimation by [2] is used for signal enhancement. Performance based on three different kinds of features is compared, i.e. for amplitude modulation spectrogram, Gabor filterbank-features and conventional Mel-frequency cepstral coefficients (MFCCs), all of them known from automatic speech recognition (ASR). The evaluation is based on the office live recordings provided within the D-CASE challenge. The influence of the signal enhancement is investigated and the increase in recognition rate by the proposed features in comparison to MFCC-features is shown. It is demonstrated that the proposed spectro-temporal feature s achieve a better recognition accuracy than MFCCs.