Enhancing Acoustic Scene Classification with Layer-wise Fine-Tuning on the SSAST Model
Shuting Hao, Daisuke Saito, Nobuaki Minematsu · 2024
We introduce a novel approach to Acoustic Scene Classification (ASC) using the Self-Supervised Audio Spectrogram Transformer (SSAST) sophisticated with a focus on layerwise fine-tuning. Recognizing the challenge in distinguishing similar classes in the TAU Urban Acoustic Scenes 2022 dataset, which sometimes exceeds human perceptual capabilities, this study introduces a novel architecture that categorizes environmental audio streams into predefined semantic labels through integrating multi-layer classifiers and direct fine-tuning. Employing the TAU Urban Acoustic Scenes 2022 Mobile dataset for both fine-tuning and validation, our SSAST model, which was initially pre-trained on the AudioSet and LibriSpeech datasets, was uniquely finetuned to enhance ASC-specific feature learning. Here, a combined approach of layer-wise and simultaneous fine-tuning of the backbone was introduced, eliminating the need for reassembling the dataset. This method achieved satisfactory results, our layered SSAST system reached an accuracy of 52.43% and an AUC of 88.51%, marking a notable improvement over the baseline with absolute increases of 1.25% in accuracy and 0.70% in AUC.