Music Structure Analysis (MSA) consists of representing a song in sections (such as ``chorus'', ``verse'', ``solo'' etc), and can be seen as the retrieval of a simplified organization of the song. This work presents a new algorithm, called Convolutive Block-Matching (CBM) algorithm, devoted to MSA. In particular, the CBM algorithm is a dynamic programming algorithm, applying on autosimilarity matrices, a standard tool in MSA. In this work, autosimilarity matrices are computed from the feature representation of an audio signal, and time is sampled on the barscale. We study three different similarity functions for the computation of autosimilarity matrices. We report that the proposed algorithm achieves a level of performance competitive to that of supervised State-of-the-Art methods on 3 among 4 metrics, while being unsupervised.

翻译：音乐结构分析（MSA）旨在将一首歌曲划分为不同段落（如“副歌”、“主歌”、“独奏”等），可视为对歌曲简化组织的检索。本文提出一种名为卷积块匹配（CBM）算法的新方法，专门用于音乐结构分析。具体而言，CBM算法是一种应用于自相似矩阵的动态规划算法，自相似矩阵是MSA中的标准工具。本研究中，自相似矩阵通过音频信号的特征表示计算，且时间以小节尺度进行采样。我们研究了三种不同的相似度函数用于自相似矩阵的计算。实验结果表明，所提算法在4项评估指标中的3项上达到了与有监督的当前最优方法相竞争的性能水平，且属于无监督方法。