Reverberation-based feature extraction for acoustic scene classification

Abstract

We present a system for acoustic scene classification, which is the task to classify an environment based on audio recordings. First, we describe a strong low-complexity baseline system using a compact feature set. Second, this system is improved with a novel class of audio features, which exploit the knowledge of sound behaviour within the scene - reverberation. This information is complementary to commonly used features for acoustic scene classification, such as spectral or cepstral components. For extracting the new features, temporal peaks in the audio signal are detected, and the decay after the peak reveals information about the reverberation properties. For the detected decays, statistics are extracted and summarized over time and over frequency bands. The combination of the novel features with features used in state-of-the-art algorithms for acoustic scene classification increases the classification accuracy, as our results obtained with a large in-house database and the DCASE 2016 database demonstrate.