For Music Information Retrieval downstream tasks, the most common audio
representation is time-frequency-based, such as Mel spectrograms. In order to
identify musical genres, this study explores the possibilities of a new form of
audio representation one of the most usual MIR downstream tasks. Therefore, to
discretely encoding music using deep vector quantization; a novel audio
representation was created for the innovative generative music model i.e.
Jukebox. The effectiveness of Jukebox's audio representation is compared to Mel
spectrograms using a dataset that is almost equivalent to State-of-the-Art
(SOTA) and an almost same transformer design. The results of this study imply
that, at least when the transformers are pretrained using a very modest dataset
of 20k tracks, Jukebox's audio representation is not superior to Mel
spectrograms. This could be explained by the fact that Jukebox's audio
representation does not sufficiently take into account the peculiarities of
human hearing perception. On the other hand, Mel spectrograms are specifically
created with the human auditory sense in mind.

通过将音乐进行深度向量量化，为创新的生成音乐模型 Jukebox 创建了一种新型的音频表示方法，并使用与最先进水平相当的数据集进行比较分析，结果显示 Jukebox 的音频表示方法在预训练使用限定数据集时不如 Mel 频谱图，这可能是因为 Jukebox 的音频表示没有充分考虑到人类听觉感知的特殊性。

音乐信息检索中用于音乐流派识别的一种新型音频表示

A Novel Audio Representation for Music Genre Identification in MIR

Transformer design is the de facto standard for natural language processing
tasks. The success of the transformer design in natural language processing has
lately piqued the interest of researchers in the domain of computer vision.
When compared to Convolutional Neural Networks (CNNs), Vision Transformers
(ViTs) are becoming more popular and dominant solutions for many vision
problems. Transformer-based models outperform other types of networks, such as
convolutional and recurrent neural networks, in a range of visual benchmarks.
We evaluate various vision transformer models in this work by dividing them
into distinct jobs and examining their benefits and drawbacks. ViTs can
overcome several possible difficulties with convolutional neural networks
(CNNs). The goal of this survey is to show the first use of ViTs in CV. In the
first phase, we categorize various CV applications where ViTs are appropriate.
Image classification, object identification, image segmentation, video
transformer, image denoising, and NAS are all CV applications. Our next step
will be to analyze the state-of-the-art in each area and identify the models
that are currently available. In addition, we outline numerous open research
difficulties as well as prospective research possibilities.

Transformer 设计是自然语言处理任务的事实标准，并且对计算机视觉领域的研究人员产生了兴趣。与卷积神经网络相比，基于 Transformer 的 Vision Transformers（ViTs）在许多视觉问题中变得更加流行和占主导地位。