Semi-Supervised Acoustic Scene Classification Under Domain Shift Using an Attention Module and Angular Loss
Michael Neri, Marco Carli · 2024
In this paper, a semi-supervised approach for the classification of audio signals under domain shift of ICME 2024 Grand Challenge is presented. In more detail, a low-complexity attention-based convolutional neural network is introduced for the identification of the scene. Specifically, it exploits the log-Mel spectrogram and the Waveg-ram learning-based time-frequency representation. Experimental results on a portion of the challenge development dataset show outstanding performance. The proposed approach achieved a macro-accuracy performance of 63.1%, outperforming the baseline by 3.1%. Code, model, and pre-trained weights are available at https://github.com/michaelneri/ICME2024RM3Team.