Audio event has a hierarchical architecture in both time and frequency and
can be grouped together to construct more abstract semantic audio classes. In
this work, we develop a multiscale audio spectrogram Transformer (MAST) that
employs hierarchical representation learning for efficient audio
classification. Specifically, MAST employs one-dimensional (and
two-dimensional) pooling operators along the time (and frequency domains) in
different stages, and progressively reduces the number of tokens and increases
the feature dimensions. MAST significantly outperforms AST~\cite{gong2021ast}
by 22.2\%, 4.4\% and 4.7\% on Kinetics-Sounds, Epic-Kitchens-100 and VGGSound
in terms of the top-1 accuracy without external training data. On the
downloaded AudioSet dataset, which has over 20\% missing audios, MAST also
achieves slightly better accuracy than AST. In addition, MAST is 5x more
efficient in terms of multiply-accumulates (MACs) with 42\% reduction in the
number of parameters compared to AST. Through clustering metrics and
visualizations, we demonstrate that the proposed MAST can learn semantically
more separable feature representations from audio signals.

这篇研究提出了一种名为多尺度音频谱变换器（MAST）的方法，它采用分层表示学习来提高音频分类的效率，相较于 AST，MAST 在没有外部训练数据的情况下，在 Kinetics-Sounds，Epic-Kitchens-100 和 VGGSound 数据集上的准确度提高了 22.2％、4.4％和 4.7％，同时比 AST 更加高效。

多尺度音频光谱变换器用于有效的音频分类

Multiscale Audio Spectrogram Transformer for Efficient Audio Classification

There is a common observation that audio event classification is easier to
deal with than detection. So far, this observation has been accepted as a fact
and we lack of a careful analysis. In this paper, we reason the rationale
behind this fact and, more importantly, leverage them to benefit the audio
event detection task. We present an improved detection pipeline in which a
verification step is appended to augment a detection system. This step employs
a high-quality event classifier to postprocess the benign event hypotheses
outputted by the detection system and reject false alarms. To demonstrate the
effectiveness of the proposed pipeline, we implement and pair up different
event detectors based on the most common detection schemes and various event
classifiers, ranging from the standard bag-of-words model to the
state-of-the-art bank-of-regressors one. Experimental results on the ITC-Irst
dataset show significant improvements to detection performance. More
importantly, these improvements are consistent for all detector-classifier
combinations.

本文针对音频事件的检测任务，通过加入高质量事件分类器，构建了一个改进的检测管道，实现了自动化验证和拒绝虚假警报，结果表明此管道在各种检测和分类器组合下，均可显著提高检测性能。