IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events ACOUSTIC EVENT DETECTION USING SIGNAL ENHANCEMENT AND SPECTRO-TEMPORAL FEATURE EXTRACTION
نویسندگان
چکیده
In this paper, an acoustic event detection system is proposed. It consists of a noise reduction signal enhancement step based on the noise power spectral density estimator proposed in [1] and on the noise suppression by [2], a Gabor filterbank feature extraction stage and a two layer hidden Markov model as back-end classifier. Optimization on the development set yields up to a F-Score of 0.73 on frame based and 0.63 on onset and offset based measure.
منابع مشابه
On the use of spectro-temporal features for the IEEE AASP challenge 'detection and classification of acoustic scenes and events'
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 sig...
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