Abstract
Music Structure Analysis (MSA) is the task aiming at identifying musical segments that compose a music track and possibly label them based on their similarity. In this paper we propose a supervised approach for the task of music boundary detection. In our approach we simultaneously learn features and convolution kernels. For this we jointly optimize -- a loss based on the Self-Similarity-Matrix (SSM) obtained with the learned features, denoted by SSM-loss, and -- a loss based on the novelty score obtained applying the learned kernels to the estimated SSM, denoted by novelty-loss. We also demonstrate that relative feature learning, through self-attention, is beneficial for the task of MSA. Finally, we compare the performances of our approach to previously proposed approaches on the standard RWC-Pop, and various subsets of SALAMI.
Abstract (translated)
音乐结构分析(MSA)的任务是确定构成音乐片段的部分,并可能根据它们的相似性将它们分类。在本文中,我们提出了一个 supervised 的方法,用于音乐边界检测任务。在我们的方法中,我们同时学习特征和卷积核。为此,我们共同优化两个损失函数:一个基于学习特征的 self-similarity-Matrix(SSM)损失,另一个基于学习核的新颖性损失,用 Novelty-loss 表示。我们还证明,通过自我关注,相对特征学习对 MSA 任务有益。最后,我们比较了我们的方法和之前提出的 approaches 在标准 RWC-Pop 音乐片段和SalAMI 各种子集上的性能。
URL
https://arxiv.org/abs/2309.02243
