AST-SED: An Effective Sound Event Detection Method Based on Audio Spectrogram Transformer

Abstract

In this paper, we propose an effective sound event detection (SED) method based on the audio spectrogram transformer (AST) model, pretrained on the large-scale AudioSet for audio tagging (AT) task, termed AST-SED. Pretrained AST models have recently shown promise on DCASE2022 challenge task4 where they help mitigate a lack of sufficient real annotated data. However, mainly due to differences between the AT and SED tasks, it is suboptimal to directly utilize outputs from a pretrained AST model. Hence the proposed AST-SED adopts an encoder-decoder architecture to enable effective and efficient fine-tuning without needing to redesign or retrain the AST model. Specifically, the Frequency-wise Transformer Encoder (FTE) consists of transformers with self attention along the frequency axis to address multiple overlapped audio events issue in a single clip. The Local Gated Recurrent Units Decoder (LGD) consists of nearest-neighbor interpolation (NNI) and Bidirectional Gated Recurrent Units (Bi-GRU) to compensate for temporal resolution loss in the pretrained AST model output. Experimental results on DCASE2022 task4 development set have demonstrated the superiority of the proposed AST-SED with FTE-LGD architecture. Specifically, the Event-Based F1-score (EB-F1) of 59.60% and Polyphonic Sound detection Score scenario1 (PSDS1) score of 0.5140 significantly outperform CRNN and other pretrained AST-based systems.