Automatic singing voice understanding tasks, such as singer identification,
singing voice transcription, and singing technique classification, benefit from
data-driven approaches that utilize deep learning techniques. These approaches
work well even under the rich diversity of vocal and noisy samples owing to
their representation ability. However, the limited availability of labeled data
remains a significant obstacle to achieving satisfactory performance. In recent
years, self-supervised learning models (SSL models) have been trained using
large amounts of unlabeled data in the field of speech processing and music
classification. By fine-tuning these models for the target tasks, comparable
performance to conventional supervised learning can be achieved with limited
training data. Therefore, in this paper, we investigate the effectiveness of
SSL models for various singing voice recognition tasks. We report the results
of experiments comparing SSL models for three different tasks (i.e., singer
identification, singing voice transcription, and singing technique
classification) as initial exploration and aim to discuss these findings.
Experimental results show that each SSL model achieves comparable performance
and sometimes outperforms compared to state-of-the-art methods on each task. We
also conducted a layer-wise analysis to further understand the behavior of the
SSL models.

本文探讨了无监督自学习模型在歌唱声音识别中的有效性和表现，并对其实验结果和行为进行了分析。

利用预训练的自监督前端实现自动唱歌声音理解任务：三个案例研究

Toward Leveraging Pre-Trained Self-Supervised Frontends for Automatic  Singing Voice Understanding Tasks: Three Case Studies

Automatic speaker verification (ASV) is one of the core technologies in
biometric identification. With the ubiquitous usage of ASV systems in
safety-critical applications, more and more malicious attackers attempt to
launch adversarial attacks at ASV systems. In the midst of the arms race
between attack and defense in ASV, how to effectively improve the robustness of
ASV against adversarial attacks remains an open question. We note that the
self-supervised learning models possess the ability to mitigate superficial
perturbations in the input after pretraining. Hence, with the goal of effective
defense in ASV against adversarial attacks, we propose a standard and
attack-agnostic method based on cascaded self-supervised learning models to
purify the adversarial perturbations. Experimental results demonstrate that the
proposed method achieves effective defense performance and can successfully
counter adversarial attacks in scenarios where attackers may either be aware or
unaware of the self-supervised learning models.

通过使用级联自监督学习模型可在 ASV 中对抗对抗攻击，并提高自身的鲁棒性。实验结果表明，该方法能在攻击者知道或不知道自监督模型的情况下有效地抵御攻击，提高了防御性能。