Acoustic detection and classification using temporal and frequency multiple energy detector features

The problem of acoustic detection and recognition is of particular interest in surveillance applications, especially in noisy environments with sound sources of different nature. Therefore, we present a multiple energy detector (MED) structure which is used to extract a new set of features for classification, called frequency MED (FMED) and combined MED (CMED). The focus of this paper is to compare these two novel feature sets with the commonly used MFCC and to evaluate their performance in a general sound classification task with different acoustic sources and adverse noise conditions. The promising results obtained show that, in low SNR, the proposed CMED features work significantly better than the MFCC.