AILabs.tw Pop1K7

AILabs.tw Pop1K7 is a dataset comprising 1747 transcribed piano performances of Western, Japanese and Korean pop songs. It was compiled in Compound Word Transformer to support research on composing expressive pop piano music at full-song length. The songs average about 4 minutes in duration, totaling 108 hours of music. All pieces are in a 4/4 time signature (four beats per bar). Each song (an audio) was converted into a symbolic sequence following specific instructions. The first file, Pop1K7.zip, contains these processed MIDI files at each step, along with their REMI and CP representations for unconditional generation tasks. The second file, Pop1K7-emo.zip, includes REMI and functional representations specifically designed for emotion-driven conditional generation tasks, as well as detected key signatures. For general conditional generation tasks, simply remove the <Emotion_*> token in each .pkl file. Please refer to the paper Compound Word Transformer for definitions of unconditional and conditional generation. Citation @inproceedings{compoundword2021, author = {Wen-Yi Hsiao and Jen-Yu Liu and Yin-Cheng Yeh and Yi-Hsuan Yang}, title = {{Compound Word Transformer}: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphs}, booktitle = {Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI}}, year = {2021}}@inproceedings{emodisentanger2024, author = {Jingyue Huang and Ke Chen and Yi-Hsuan Yang}, title = {Emotion-driven Piano Music Generation via Two-stage Disentanglement and Functional Representation}, booktitle = {Proceedings of the International Society for Music Information Retrieval Conference, {ISMIR}}, year = {2024} }AILabs.tw 推出的 Pop1K7 数据集，收录了1747首经转录的钢琴演奏作品，涵盖西方、日本及韩国流行曲目。本数据集依托 Compound Word Transformer（复合词Transformer）构建，旨在支撑全曲长度的富有表现力的流行钢琴音乐创作相关研究。所有乐曲平均时长约4分钟，总播放时长共计108小时，且全部采用4/4拍（每小节4拍）。每首音频格式的乐曲均按照特定规范转换为符号序列。 首个压缩文件 Pop1K7.zip 包含各处理步骤对应的MIDI文件，以及用于无条件生成任务的REMI与CP表征。第二个压缩文件 Pop1K7-emo.zip 则收录了专为情感驱动的条件生成任务设计的REMI与函数表征，以及自动检测得到的调号信息。若需开展通用条件生成任务，仅需移除每个.pkl文件中的<Emotion_*>标记即可。有关无条件生成与条件生成的定义，请参阅论文《Compound Word Transformer》。 引用 @inproceedings{compoundword2021, author = {Wen-Yi Hsiao and Jen-Yu Liu and Yin-Cheng Yeh and Yi-Hsuan Yang}, title = {{Compound Word Transformer}: 在动态有向超图上学习创作全曲音乐}, booktitle = {第三十五届 AAAI 人工智能大会（AAAI）}, year = {2021} } @inproceedings{emodisentanger2024, author = {Jingyue Huang and Ke Chen and Yi-Hsuan Yang}, title = {基于两阶段解耦与函数表征的情感驱动钢琴音乐生成}, booktitle = {国际音乐信息检索大会（ISMIR）论文集}, year = {2024} }