REVIEW 2 cited by
miditok: A Python package for MIDI file tokenization
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Recent progress in natural language processing has been adapted to the symbolic music modality. Language models, such as Transformers, have been used with symbolic music for a variety of tasks among which music generation, modeling or transcription, with state-of-the-art performances. These models are beginning to be used in production products. To encode and decode music for the backbone model, they need to rely on tokenizers, whose role is to serialize music into sequences of distinct elements called tokens. MidiTok is an open-source library allowing to tokenize symbolic music with great flexibility and extended features. It features the most popular music tokenizations, under a unified API. It is made to be easily used and extensible for everyone.
Forward citations
Cited by 2 Pith papers
-
BeatEdit: Symbolic Music Generation as Explicit Editing
Explicit edit operations on Beat encoding outperform AR and diffusion on music error correction, accompaniment editing, and segment completion while running under 100 ms.
-
RPPNet: Perceptually-Grouped Rhythm-Pitch Primitives for Long-Term Structure Melody Generation via Boundary-Aware Modeling
RPPNet generates melodies by planning variable-length perceptually grouped rhythm-pitch primitives first and then decoding them into notes, beating bar-level baselines in subjective structure and musicality ratings.
Discussion (0). Continue with ORCID to comment.