Self-supervision methods learn representations by solving pretext tasks that
do not require human-generated labels, alleviating the need for time-consuming
annotations. These methods have been applied in computer vision, natural
language processing, environmental sound analysis, and recently in music
information retrieval, e.g. for pitch estimation. Particularly in the context
of music, there are few insights about the fragility of these models regarding
different distributions of data, and how they could be mitigated. In this
paper, we explore these questions by dissecting a self-supervised model for
pitch estimation adapted for tempo estimation via rigorous experimentation with
synthetic data. Specifically, we study the relationship between the input
representation and data distribution for self-supervised tempo estimation.

本文通过实验分析自监督的音高估计模型在节奏估计中的适用性和鲁棒性，探究了输入表示和数据分布之间的关系。

节奏与音高：自监督节奏估计的理解

Tempo vs. Pitch: understanding self-supervised tempo estimation

Sounds, especially music, contain various harmonic components scattered in
the frequency dimension. It is difficult for normal convolutional neural
networks to observe these overtones. This paper introduces a multiple rates
dilated causal convolution (MRDC-Conv) method to capture the harmonic structure
in logarithmic scale spectrograms efficiently. The harmonic is helpful for
pitch estimation, which is important for many sound processing applications. We
propose HarmoF0, a fully convolutional network, to evaluate the MRDC-Conv and
other dilated convolutions in pitch estimation. The results show that this
model outperforms the DeepF0, yields state-of-the-art performance in three
datasets, and simultaneously reduces more than 90% parameters. We also find
that it has stronger noise resistance and fewer octave errors. The code and
pre-trained model are available at this https URL.

该研究提出了一种多速率膨胀因果卷积（MRDC-Conv）方法，通过对数尺度频谱图高效捕捉谐波结构，从而帮助提升音高估计的性能和准确度，并与其他膨胀卷积方法进行了比较，结果表明该模型在三个数据集中表现出色，能够有效降低大约 90% 的参数，同时具有强大的抗噪能力和更少的八度错误。