Novel sound event and sound activity detection framework based on intrinsic mode functions and deep learning

Abstract

The detection of sound events has become increasingly important due to the development of signal processing methods, social media, and the need for automatic labeling methods in applications such as smart cities, navigation, and security systems. For example, in such applications, it is often important to detect sound events at different levels, such as the presence or absence of an event in the segment, or to specify the beginning and end of the sound event and its duration. This study proposes a method to reduce the feature dimensions of a Sound Event Detection (SED) system while maintaining the systems efficiency. The proposed method, using Empirical Mode Decomposition (EMD), Intrinsic Mode Functions (IMFs), and extraction of locally regulated features from different IMFs of the signal, shows a promising performance relative to the conventional features of SED systems. In addition, the feature dimensions of the proposed method are much smaller than those of conventional methods. To prove the effectiveness of the proposed features in SED tasks, two segment-based approaches for event detection and sound activity detection were implemented using the suggested features, and their effectiveness was confirmed. Simulation results on the URBAN SED dataset showed that the proposed approach reduces the number of input features by more than 99% compared with state-of-the-art methods while maintaining accuracy. According to the obtained results, the proposed method is quite promising.