Paper year
2025
Paper dossier
Multi-modal music techniques for synthesizing high-quality audio waveforms from MIDI data
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Citations
0
Authors
0
Topic labels
0
Source readout
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Unknown venue
Source slug
unknown
Corpus placement
Controlled edge slice
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Run shadow-generalization-product-candidate-ranking-v1Top 50 surfaced
This block uses the same resolved ranking run as Recommended. Ranks here are materialized paper_scores ranks; live Emerging may be reordered by the bounded ML scorer. Family rank is global within each family, but rank is only shown when this paper lands inside the surfaced top 50.
Families present
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=0.8627, 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: 0.1725)
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
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)
Under-cited
No materialized row for this family in the resolved run
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Abstract
A highly effective music synthesizer should deliver high-fidelity audio for a mix of instruments and voices. Current synthesizers often need to choose between specialized models that provide detailed control over specific instruments and flexible waveform models that accommodate a variety of music at the expense of precision. To transcend the existing limitations, this paper introduces MIAO, an avant-garde neural music synthesizer that revolutionizes the domain of interactive and expressive music synthesis by converting MIDI sequences into rich, dynamic audio outputs. Specifically, MIAO can be cultivated through training on diverse transcription datasets that correlate MIDI with audio, thereby deepening its comprehension of MIDI intricacies and elevating its capacity for robust representation learning. This approach allows MIAO to offer precise note-level control over composition and instrumentation, effectively handling a wide spectrum of instruments. We evaluate MIAO's performance by benchmarking it against six datasets: MAESTROv3 (piano), Slakh2100 (synthetic multi-instrument), Cerberus4 (synthetic multi-instrument), Guitarset (guitar), MusicNet (orchestral multi-instrument), and URMP (orchestral multi-instrument), where it sets new performance benchmarks.
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