论文标题
音乐模式发现算法的计算评估
A Computational Evaluation of Musical Pattern Discovery Algorithms
论文作者
论文摘要
音乐领域中的图案发现算法旨在在音乐作品中找到有意义的组成部分。多年来，尽管已经为音乐数据中的模式发现开发了许多算法，但它仍然是一项艰巨的任务。为了更多地了解这些算法的功效，我们引入了三种计算方法来检查其输出：模式轮询，以结合模式；比较分类，以区分模式；合成数据，以注入预定的模式。在结合和区分算法提取的模式时，我们暴露了它们与人类和算法自身之间注释的模式的不同之处，以及节奏特征对算法 - 人类和算法 - 算法 - 算法差异最大。尽管很难核对和评估从算法中提取的不同模式，但我们确定了解决这些算法的一些可能性。特别是，我们生成可控制的合成数据，并使用预定的模式进行随机数据，从而使我们能够更好地检查，比较，验证和选择算法。我们提供了合成数据的具体示例，以理解算法并将讨论扩展到这种方法的潜在和局限性。
Pattern discovery algorithms in the music domain aim to find meaningful components in musical compositions. Over the years, although many algorithms have been developed for pattern discovery in music data, it remains a challenging task. To gain more insight into the efficacy of these algorithms, we introduce three computational methods for examining their output: Pattern Polling, to combine the patterns; Comparative Classification, to differentiate the patterns; Synthetic Data, to inject predetermined patterns. In combining and differentiating the patterns extracted by algorithms, we expose how they differ from the patterns annotated by humans as well as between algorithms themselves, with rhythmic features contributing the most to the algorithm-human and algorithm-algorithm discrepancies. Despite the difficulty in reconciling and evaluating the divergent patterns extracted from algorithms, we identify some possibilities for addressing them. In particular, we generate controllable synthesised data with predetermined patterns planted into random data, thereby leaving us better able to inspect, compare, validate, and select the algorithms. We provide a concrete example of synthesising data for understanding the algorithms and expand our discussion to the potential and limitations of such an approach.
