Paper year
2025
Paper dossier
MIDI-Zero: A MIDI-driven Self-Supervised Learning Approach for Music Retrieval
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unknown
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Run shadow-generalization-product-candidate-ranking-v1Top 50 surfaced
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2
Top 50
0
Run label
shadow-generalization-product-candidate-ranking-v1
Snapshot
source-snapshot-shadow-generalization-v1-20260521
Scope: family global | run rank-83787b91ef
Emerging
Present in run, outside top 50
Emerging: embedding slice fit vs included-corpus centroid (title+abstract), plus citation velocity and topic growth; not universal relevance. Bridge signal not used here.
Signals: semantic=0.8584, citation_velocity=0.0000, topic_growth=0.0000, diversity_penalty=0.0000
Why this surfaced | 3 used | 1 penalty | 1 not computed
Embedding slice fit (corpus centroid)used
Embedding slice fit (corpus centroid): high; used in final ranking (contribution to score: 0.1717)
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentumused
Topic momentum: low; used in final ranking (contribution to score: 0.0000)
Cross-cluster signalnot computed
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Similarity penaltypenalty
Similarity penalty: reduces score when non-zero (contribution to score: 0.0000)
Bridge
Present in run, outside top 50
Multi-topic paper in active topics; no cluster_version on this run so bridge_score was not computed.
Signals: citation_velocity=0.0000, topic_growth=0.0000, diversity_penalty=1.0000
Why this surfaced | 2 used | 1 penalty | 2 not computed
Semantic matchnot computed
Semantic match: not computed for this run
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentumused
Topic momentum: low; used in final ranking (contribution to score: 0.0000)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Topic breadth penaltypenalty
Topic breadth penalty: reduces score when non-zero (contribution to score: -0.2000)
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Abstract
Content-based Music Retrieval (CBMR) is a fundamental task in music information retrieval, encompassing sub-tasks including Audio Identification, Audio Matching, and Version Identification. Traditional methods typically analyze audio signals or spectrograms to extract features related to rhythm, melody, harmony, and timbre. However, with the rapid development of Music Transcription and digital music technologies, MIDI representation has emerged as a powerful alternative fo r music analysis. In this paper, we propose MIDI-Zero, a novel self-supervisedlearning framework for CBMR that operates entirely on MIDI representations. Unlike existing approaches, MIDI-Zero requires no external training data; all training data is automatically generated based on predefined task rules, eliminating the need for labeled datasets or external music collections. MIDI-Zero is designed to handle both symbolic music data and audio-based tasks by leveraging Music Transcription models. Its strong robustness ensures effectiveness even with low-quality transcriptions. Extensive experiments demonstrate that MIDI-Zero achieves competitive performance across various CBMR sub-tasks, particularly excelling in Audio Matching. Our approach simplifies the feature extraction process, bridges the gap between audio and symbolic music representations, and offers a versatile and scalable solution for music retrieval.
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