Self-supervised learning has emerged as a powerful way to pre-train
generalizable machine learning models on large amounts of unlabeled data. It is
particularly compelling in the music domain, where obtaining labeled data is
time-consuming, error-prone, and ambiguous. During the self-supervised process,
models are trained on pretext tasks, with the primary objective of acquiring
robust and informative features that can later be fine-tuned for specific
downstream tasks. The choice of the pretext task is critical as it guides the
model to shape the feature space with meaningful constraints for information
encoding. In the context of music, most works have relied on contrastive
learning or masking techniques. In this study, we expand the scope of pretext
tasks applied to music by investigating and comparing the performance of new
self-supervised methods for music tagging. We open-source a simple ResNet model
trained on a diverse catalog of millions of tracks. Our results demonstrate
that, although most of these pre-training methods result in similar downstream
results, contrastive learning consistently results in better downstream
performance compared to other self-supervised pre-training methods. This holds
true in a limited-data downstream context.

在音乐领域应用自监督学习，通过研究和比较新的自监督方法，证明对比学习相对于其他自监督预训练方法在有限数据下游任务中始终能够取得更好的性能。

多视图自监督方法在音乐标记中的实验比较

An Experimental Comparison Of Multi-view Self-supervised Methods For  Music Tagging

In this work, we provide a broad comparative analysis of strategies for
pre-training audio understanding models for several tasks in the music domain,
including labelling of genre, era, origin, mood, instrumentation, key, pitch,
vocal characteristics, tempo and sonority. Specifically, we explore how the
domain of pre-training datasets (music or generic audio) and the pre-training
methodology (supervised or unsupervised) affects the adequacy of the resulting
audio embeddings for downstream tasks.
We show that models trained via supervised learning on large-scale
expert-annotated music datasets achieve state-of-the-art performance in a wide
range of music labelling tasks, each with novel content and vocabularies. This
can be done in an efficient manner with models containing less than 100 million
parameters that require no fine-tuning or reparameterization for downstream
tasks, making this approach practical for industry-scale audio catalogs.
Within the class of unsupervised learning strategies, we show that the domain
of the training dataset can significantly impact the performance of
representations learned by the model. We find that restricting the domain of
the pre-training dataset to music allows for training with smaller batch sizes
while achieving state-of-the-art in unsupervised learning -- and in some cases,
supervised learning -- for music understanding.
We also corroborate that, while achieving state-of-the-art performance on
many tasks, supervised learning can cause models to specialize to the
supervised information provided, somewhat compromising a model's generality.

本文提供了音频理解模型预训练策略的广泛比较分析，探讨了预训练数据集（音乐或通用音频）和预训练方法（有监督或无监督）对下游任务的影响，特别是在音乐领域的多项任务中，超大规模有人工注释的音乐数据集上训练的有监督模型实现了最先进的性能，而域限制在音乐领域的无监督模型则在某些情况下能实现优异的有监督学习和无监督学习性能，表现出较高的效率和通用性。

音乐理解的有监督和无监督音频表示学习

Supervised and Unsupervised Learning of Audio Representations for Music Understanding

Reviews of songs play an important role in online music service platforms.
Prior research shows that users can make quicker and more informed decisions
when presented with meaningful song reviews. However, reviews of music songs
are generally long in length and most of them are non-informative for users. It
is difficult for users to efficiently grasp meaningful messages for making
decisions. To solve this problem, one practical strategy is to provide tips,
i.e., short, concise, empathetic, and self-contained descriptions about songs.
Tips are produced from song reviews and should express non-trivial insights
about the songs. To the best of our knowledge, no prior studies have explored
the tip generation task in music domain. In this paper, we create a dataset
named MTips for the task and propose a framework named GENTMS for automatically
generating tips from song reviews. The dataset involves 8,003 Chinese
tips/non-tips from 128 songs which are distributed in five different song
genres. Experimental results show that GENTMS achieves top-10 precision at
85.56%, outperforming the baseline models by at least 3.34%. Besides, to
simulate the practical usage of our proposed framework, we also experiment with
previously-unseen songs, during which GENTMS also achieves the best performance
with top-10 precision at 78.89% on average. The results demonstrate the
effectiveness of the proposed framework in tip generation of the music domain.

在音乐领域，我们提出了 GENTMS 框架来生成歌曲点评中的 tips 并创建了一个名为 MTips 的数据集。实验结果表明，我们的模型在新歌曲上的表现仍优秀并显示出在在线音乐服务平台中的应用前景。