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

• Main Authors: Schroder, Jens, Moritz, Niko, Schadler, Marc Rene, Cauchi, Benjamin, Adiloglu, Kamil, Anemuller, Jorn, Doclo, Simon, Kollmeier, Birger, Goetze, Stefan
• Format: Conference Proceeding
• Language: English
• Subjects: acoustic event detection; Acoustics; amplitude modulation spectrogram; Event detection; Feature extraction; Frequency modulation; Gabor filterbank; Hidden Markov models; IEEE AASP D-CASE challenge; Noise; Speech
• ISSN: 1931-1168; 1947-1629
• DOI: 10.1109/WASPAA.2013.6701868

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 features achieve a better recognition accuracy than MFCCs.