Computational auditory scene recognition

In this paper, we address the problem of computational auditory scene recognition and describe methods to classify auditory scenes into predefined classes. By auditory scene recognition we mean recognition of an environment using audio information only. The auditory scenes comprised tens of everyday outside and inside environments, such as streets, restaurants, offices, family homes, and cars. Two completely different but almost equally effective classification systems were used: band-energy ratio features with 1-NN classifier and Mel-frequency cepstral coefficients with Gaussian mixture models. The best obtained recognition rate for 17 different scenes out of 26 and for an analysis duration of 30 seconds was 68.4%. For comparison, the recognition accuracy of humans was 70% for 25 different scenes and the average response time was around 20 seconds. The efficiency of different acoustic features and the effect of test sequence length were studied.