| Autor: |
Kamil Adiloglu, Niko Moritz, Jörn Anemüller, Jens Schroder, Marc René Schädler, Stefan Goetze, Simon Doclo, Benjamin Cauchi, Birger Kollmeier |
| Rok vydání: |
2013 |
| Předmět: |
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| Zdroj: |
WASPAA |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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