Sound Event Detection in Domestic Environment Using Frequency-Dynamic Convolution and Local Attention

This work describes a methodology for sound event detection in domestic environments. Efficient solutions in this task can support the autonomous living of the elderly. The methodology deals with the “Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE)” 2023, and more specifically with Task 4a “Sound event detection of domestic activities”. This task involves the detection of 10 common events in domestic environments in 10 s sound clips. The events may have arbitrary duration in the 10 s clip. The main components of the methodology are data augmentation on mel-spectrograms that represent the sound clips, feature extraction by passing spectrograms through a frequency-dynamic convolution network with an extra attention module in sequence with each convolution, concatenation of these features with BEATs embeddings, and use of BiGRU for sequence modeling. Also, a mean teacher model is employed for leveraging unlabeled data. This research focuses on the effect of data augmentation techniques, of the feature extraction models, and on self-supervised learning. The main contribution is the proposed feature extraction model, which uses weighted attention on frequency in each convolution, combined in sequence with a local attention module adopted by computer vision. The proposed system features promising and robust performance.