We have collected the most relevant information on Ast Audio Transformers. Open the URLs, which are collected below, and you will find all the info you are interested in.
AST: Audio Spectrogram Transformer - GitHub
https://github.com/YuanGongND/ast
Introduction. This repository contains the official implementation (in PyTorch) of the Audio Spectrogram Transformer (AST) proposed in the Interspeech 2021 paper AST: Audio Spectrogram Transformer (Yuan Gong, Yu-An Chung, James Glass). AST is the first convolution-free, purely attention-based model for audio classification which supports ...
AST: Audio Spectrogram Transformer | Papers With Code
https://paperswithcode.com/paper/ast-audio-spectrogram-transformer
AST: Audio Spectrogram Transformer
https://pythonawesome.com/ast-audio-spectrogram-transformer/
This repository contains the official implementation (in PyTorch) of the Audio Spectrogram Transformer (AST) proposed in the Interspeech 2021 paper AST: Audio Spectrogram Transformer (Yuan Gong, Yu-An Chung, James Glass). AST is the first convolution-free, purely attention-based model for audio classification which supports variable length input …
[2104.01778v1] AST: Audio Spectrogram Transformer
https://arxiv.org/abs/2104.01778v1
In this paper, we answer the question by introducing the Audio Spectrogram Transformer (AST), the first convolution-free, purely attention-based model for audio classification. We evaluate AST on various audio classification benchmarks, where it achieves new state-of-the-art results of 0.485 mAP on AudioSet, 95.6% accuracy on ESC-50, and 98.1% …
AST: Audio Spectrogram Transformer
https://www.isca-speech.org/archive/pdfs/interspeech_2021/gong21b_interspeech.pdf
tion by introducing the Audio Spectrogram Transformer (AST), the first convolution-free, purely attention-based model for au-dio classification. We evaluate AST on various audio classifi-cation benchmarks, where it achieves new state-of-the-art re-sults of 0.485 mAP on AudioSet, 95.6% accuracy on ESC-50, and 98.1% accuracy on Speech Commands V2.
AST: Audio Spectrogram Transformer – arXiv Vanity
https://www.arxiv-vanity.com/papers/2104.01778/
In this paper, we answer the question by introducing the Audio Spectrogram Transformer (AST), the first convolution-free, purely attention-based model for audio classification. We evaluate AST on various audio classification benchmarks, where it achieves new state-of-the-art results of 0.485 mAP on AudioSet, 95.6% accuracy on ESC-50, and 98.1% accuracy on Speech …
(PDF) AST: Audio Spectrogram Transformer
https://www.researchgate.net/publication/350647165_AST_Audio_Spectrogram_Transformer
Specifically, the Audio Spectrogram Transformer (AST) achieves state-of-the-art results on various audio classification benchmarks.
SSAST: Self-Supervised Audio Spectrogram Transformer
https://arxiv.org/abs/2110.09784
Specifically, the Audio Spectrogram Transformer (AST) achieves state-of-the-art results on various audio classification benchmarks. However, pure Transformer models tend to require more training data compared to CNNs, and the success of the AST relies on supervised pretraining that requires a large amount of labeled data and a complex training pipeline, thus …
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