Improving event detection for audio surveillance using Gabor filterbank features

Acoustic event detection in surveillance scenarios is an important but difficult problem. Realistic systems are struggling with noisy recording conditions. In this work, we propose to use Gabor filterbank features to detect target events in different noisy background scenes. These features capture spectro-temporal modulation frequencies in the signal, which makes them suited for the detection of non-stationary sound events. A single-class detector is constructed for each of the different target events. In a hierarchical framework, the separate detectors are combined to a multi-class detector. Experiments are performed using a database of four different target sounds and four background scenarios. On average, the proposed features outperform conventional features in all tested noise levels, in terms of detection and classification performance.