Jazz pianists often uniquely interpret jazz standards. Passages from these interpretations can be viewed as sections of variation. We manually extracted such variations from solo jazz piano performances. The JAZZVAR dataset is a collection of 502 pairs of Variation and Original MIDI segments. Each Variation in the dataset is accompanied by a corresponding Original segment containing the melody and chords from the original jazz standard. Our approach differs from many existing jazz datasets in the music information retrieval (MIR) community, which often focus on improvisation sections within jazz performances. In this paper, we outline the curation process for obtaining and sorting the repertoire, the pipeline for creating the Original and Variation pairs, and our analysis of the dataset. We also introduce a new generative music task, Music Overpainting, and present a baseline Transformer model trained on the JAZZVAR dataset for this task. Other potential applications of our dataset include expressive performance analysis and performer identification.

翻译：爵士钢琴家通常对爵士标准曲目进行独特的诠释。这些诠释中的乐段可视为变奏片段。我们从独奏爵士钢琴演奏中手动提取了此类变奏。JAZZVAR数据集包含502对变奏与原始MIDI片段，每个变奏片段对应一个包含原始爵士标准曲目旋律与和弦的原始片段。与音乐信息检索（MIR）学界现有许多侧重爵士演奏即兴片段的爵士数据集不同，本文概述了曲目获取与整理的策展流程、创建原创与变奏片段的处理管线，以及对该数据集的分析。我们同时引入了一项新的生成式音乐任务——音乐覆盖（Music Overpainting），并提出了基于JAZZVAR数据集训练的基线Transformer模型。该数据集的其他潜在应用包括表现力分析与演奏者识别。