IdolSongsJp 语料库：日本偶像团体风格的多歌手歌曲语料库
IdolSongsJp Corpus: A Multi-Singer Song Corpus in the Style of Japanese Idol Groups
AI总结：
本文构建了 IdolSongsJp 语料库，包含 15 首日本偶像团体风格的专业创作歌曲及分轨、干声和和弦标注，用于在复杂条件下基准测试歌手分离、音源分离和和弦估计等音乐信息处理技术。
摘要：
日本偶像团体由被称为“偶像”的表演者组成，是日本流行文化中不可或缺的一部分。他们经常出现在现场演唱会和电视节目中，以歌唱和舞蹈娱乐观众。与其他 J-pop 歌曲类似，偶像团体音乐涵盖广泛的风格，具有各种类型的和弦进行和器乐编配。这些曲目通常包含大量乐器，并采用复杂的母带处理技术，从而产生较高的信号响度。此外，大多数歌曲包含歌曲分段（utawari）结构，成员在独唱和合唱之间交替进行。因此，这些歌曲非常适合在具有挑战性的条件下对各种音乐信息处理技术进行基准测试，例如歌手分离、音乐源分离和自动和弦估计。针对这些特点，我们委托专业作曲家创作了 15 首日本偶像团体风格的曲目，构建了名为 IdolSongsJp 的歌曲语料库。该语料库不仅包含母带处理后的音频轨，还包含用于音乐源分离的分轨、干声轨以及和弦标注。本文详细描述了该语料库，通过与真实偶像团体歌曲的比较展示了其多样性，并展示了其在评估多种音乐信息处理技术中的应用。
英文摘要：
Japanese idol groups, comprising performers known as "idols," are an indispensable part of Japanese pop culture. They frequently appear in live concerts and television programs, entertaining audiences with their singing and dancing. Similar to other J-pop songs, idol group music covers a wide range of styles, with various types of chord progressions and instrumental arrangements. These tracks often feature numerous instruments and employ complex mastering techniques, resulting in high signal loudness. Additionally, most songs include a song division (utawari) structure, in which members alternate between singing solos and performing together. Hence, these songs are well-suited for benchmarking various music information processing techniques such as singer diarization, music source separation, and automatic chord estimation under challenging conditions. Focusing on these characteristics, we constructed a song corpus titled IdolSongsJp by commissioning professional composers to create 15 tracks in the style of Japanese idol groups. This corpus includes not only mastered audio tracks but also stems for music source separation, dry vocal tracks, and chord annotations. This paper provides a detailed description of the corpus, demonstrates its diversity through comparisons with real-world idol group songs, and presents its application in evaluating several music information processing techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
