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arXiv · 2305.17834

Streaming Audio Transformers for Online Audio Tagging

Heinrich Dinkel·Zhiyong Yan·Yongqing Wang·Junbo Zhang·Yujun Wang·Bin Wang
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Abstract

Transformers have emerged as a prominent model framework for audio tagging (AT), boasting state-of-the-art (SOTA) performance on the widely-used Audioset dataset. However, their impressive performance often comes at the cost of high memory usage, slow inference speed, and considerable model delay, rendering them impractical for real-world AT applications. In this study, we introduce streaming audio transformers (SAT) that combine the vision transformer (ViT) architecture with Transformer-Xl-like chunk processing, enabling efficient processing of long-range audio signals. Our proposed SAT is benchmarked against other transformer-based SOTA methods, achieving significant improvements in terms of mean average precision (mAP) at a delay of 2s and 1s, while also exhibiting significantly lower memory usage and computational overhead. Checkpoints are publicly available https://github.com/RicherMans/SAT.

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Heinrich Dinkel, Zhiyong Yan, Yongqing Wang, Junbo Zhang, Yujun Wang, Bin Wang. 2023-05-29. Streaming Audio Transformers for Online Audio Tagging. https://arxiv.org/abs/2305.17834

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