IdolSongsJp Corpus: A Multi-Singer Song Corpus in the Style of Japanese Idol Groups

2025-07-02

AI Summary

To address the lack of high-quality benchmark corpora for Japanese idol-group songs in tasks such as singer diarization, source separation, and chord estimation, this work introduces IdolSongsJp: a corpus comprising 15 professionally produced idol-style songs, each annotated with master recordings, isolated dry vocals, multitrack stems, and manually verified chord labels. We systematically model idol-specific musical characteristics—including structured alternation between solo and choral sections and high-loudness mixing—using a hybrid annotation strategy that integrates professional audio production practices with rigorous signal analysis, ensuring fidelity and reproducibility. Empirical evaluation confirms that the corpus exhibits acoustic diversity closely matching real-world idol repertoire. IdolSongsJp has already enabled robustness assessment of multiple MIR models under challenging multi-singer conditions, substantially enhancing the reliability and task-specific relevance of benchmarking in this niche domain.

Technology Category

• Humans and AI: Crowd Sourcing and Human Computation
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Benchmark music processing for Japanese idol songs
• Address singer diarization in complex idol tracks
• Evaluate source separation in multi-instrument idol music

Innovation

• Professional composers created 15 idol-style tracks
• Includes stems, dry vocals, and chord annotations
• Benchmarks music processing under challenging conditions