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arXiv · 2211.02250

Real-Time Target Sound Extraction

Bandhav Veluri·Justin Chan·Malek Itani·Tuochao Chen·Takuya Yoshioka·Shyamnath Gollakota
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Abstract

We present the first neural network model to achieve real-time and streaming target sound extraction. To accomplish this, we propose Waveformer, an encoder-decoder architecture with a stack of dilated causal convolution layers as the encoder, and a transformer decoder layer as the decoder. This hybrid architecture uses dilated causal convolutions for processing large receptive fields in a computationally efficient manner while also leveraging the generalization performance of transformer-based architectures. Our evaluations show as much as 2.2-3.3 dB improvement in SI-SNRi compared to the prior models for this task while having a 1.2-4x smaller model size and a 1.5-2x lower runtime. We provide code, dataset, and audio samples: https://waveformer.cs.washington.edu/.

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BibTeXRIS

Bandhav Veluri, Justin Chan, Malek Itani, Tuochao Chen, Takuya Yoshioka, Shyamnath Gollakota. 2022-11-04. Real-Time Target Sound Extraction. https://arxiv.org/abs/2211.02250

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