Current approaches for explaining deep learning systems applied to musical data provide results in a low-level feature space, e.g., by highlighting potentially relevant time-frequency bins in a spectrogram or time-pitch bins in a piano roll. This can be difficult to understand, particularly for musicologists without technical knowledge. To address this issue, we focus on more human-friendly explanations based on high-level musical concepts. Our research targets trained systems (post-hoc explanations) and explores two approaches: a supervised one, where the user can define a musical concept and test if it is relevant to the system; and an unsupervised one, where musical excerpts containing relevant concepts are automatically selected and given to the user for interpretation. We demonstrate both techniques on an existing symbolic composer classification system, showcase their potential, and highlight their intrinsic limitations.

翻译：目前用于音乐数据的深层次学习系统的解释方法,在低层次的地貌空间中提供了成果,例如,在光谱或钢琴卷中突出可能相关的时间频率垃圾桶,这可能难以理解,特别是对于没有技术知识的音乐学家来说。为了解决这一问题,我们侧重于基于高级音乐概念的更人性友好的解释。我们的研究目标是经过培训的系统(热后解释),并探索两种方法:一种是受监督的系统,用户可以据此定义音乐概念,如果与系统有关,则进行测试;另一种是不受监督的系统,自动选择含有相关概念的音乐节录,并提供给用户解释。我们用现有的象征性作曲师分类系统展示这两种技术,展示其潜力,并突出其内在局限性。