Transformer 由多个编码器层和解码器层组成。由于 AST 是为分类任务设计的,因此我们只使用 Transformer 的编码器。 我们使用原始的 Transformer 编码器 [18] 架构而不进行修改。这种简单设置的优点是 1) 标准 Transformer 架构很容易实现和重现,因为它在 TensorFlow 和 PyTorch 中是现成的 2) 我们打算将迁移学习应用...
1. Background and Motivation: 最近CNN+Transformer 的混合框架开始盛行,作者提出一个疑问:如果 Transformer 已经可以获得较好的结果了,那么是否还要使用 CNN 呢?作者提出了一个完全是 self-attention 的网络来处理音频信息,所提出的方法称为 Audio Spectrogram Transformer (AST)。作者总结了如下几点优势: 1). 性能好...
1. Background and Motivation: 最近CNN+Transformer 的混合框架开始盛行,作者提出一个疑问:如果 Transformer 已经可以获得较好的结果了,那么是否还要使用 CNN 呢?作者提出了一个完全是 self-attention 的网络来处理音频信息,所提出的方法称为 Audio Spectrogram Transformer (AST)。作者总结了如下几点优势: 1). 性能好...
由于transformer 不能获取序列信息,我们还在时间维度上增加了可学习位置的embedingEt∈R(100T+1)×768Et∈R(100T+1)×768,或者在频率-维嵌入上增加了Ef∈R129×768Ef∈R129×768。 最后,将序列Et∈R(100T+1)×768Et∈R(100T+1)×768,或者Ef∈R129×768Ef∈R129×768, 输入到transformer 块中进行分类。
Audio Spectrogram Transformer model is Vision transformer model which turns audio into an image(spectrogram). The following code example uses the huggingface pre-trained AST model to show that this...
which reduces the dependency on large amounts of labeled data and focuses on extracting concise representation of the audio spectrograms. In this paper, we propose ASiT, a novel self-supervised transformer for general audio representations that captures local and global contextual information employing...
音频编码器选择了 AST (Audio spectrogram transformer)。 多模态的 decoder 选择了 BERT 模型,此外在self-attention和FFNN 之间添加了 cross-attention 层,看起来就是 原始的 Transformer Decoder,但是要注意 self-attention 和 FFNN 的参数和文本编码器是共享的。 预训练任务 本文提出了两个预训练任务: Multimodal ...
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...
2 Nov 2022·Sreyan Ghosh,Ashish Seth,S. Umesh,Dinesh Manocha· We present Multiscale Audio Spectrogram Transformer (MAST) for audio classification, which brings the concept of multiscale feature hierarchies to the Audio Spectrogram Transformer (AST). Given an input audio spectrogram, we first patc...
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...