A key aspect of machine learning models lies in their ability to learn efficient intermediate features. However, the input representation plays a crucial role in this process, and polyphonic musical scores remain a particularly complex type of information. In this paper, we introduce a novel representation of symbolic music data, which transforms a polyphonic score into a continuous signal. We evaluate the ability to learn meaningful features from this representation from a musical point of view. Hence, we introduce an evaluation method relying on principled generation of synthetic data. Finally, to test our proposed representation we conduct an extensive benchmark against recent polyphonic symbolic representations. We show that our signal-like representation leads to better reconstruction and disentangled features. This improvement is reflected in the metric properties and in the generation ability of the space learned from our signal-like representation according to music theory properties.
翻译:机器学习模型的一个关键方面在于它们学习高效中间特征的能力。然而,输入代表在这个过程中发挥着关键作用,多声乐评分仍然是特别复杂的信息类型。在本文件中,我们引入了象征性音乐数据的新表述,将多声乐评分转化为连续信号。我们从音乐的角度评价从这一表述中学习有意义特征的能力。因此,我们引入了一种依赖有原则的合成数据生成的评价方法。最后,为了检验我们拟议的代表,我们根据最近的多声乐象征性表述进行了广泛的基准。我们表明,我们信号相似的表述导致更好的重建和分解特征。这种改进体现在根据音乐理论属性从我们信号相似的表述中学习的空间的度属性和生成能力。