arXiv:2610.01864v1 Announce Type: cross Abstract: How can we understand what a music foundation model has learned \textit{internally}? Most interpretability approaches, such as probing and Sparse Aut...
We study the assignment of local tonalities to chord sequences, a task useful for harmonic analysis, composition, and jazz-oriented improvisation. Standard dynamic-programming approaches minimize modulations but can introduce unnecessarily many tonal centers.
arXiv:2606. 03459v1 Announce Type: cross Abstract: We study the assignment of local tonalities to chord sequences, a task useful for harmonic analysis, composition, and jazz-oriented improvisation.
Chordonomicon is a new dataset of over 666,000 song-level symbolic chord progressions, each annotated with structural parts such as verse, chorus, and bridge, as well as genre and release date. The dataset was compiled by scraping user-generated progressions from multiple sources and shows strong similarity to established prior datasets. The authors also provide a reproducible benchmark suite for next chord prediction, evaluating RNN, GRU, and LSTM models across various context windows and data scales, and find that structural part annotations consistently improve prediction performance.
arXiv:2607. 19776v1 Announce Type: cross Abstract: Existing symbolic music generation models typically use bars as the basic structural unit.
arXiv:2607. 14537v1 Announce Type: cross Abstract: Rich internal representations of musical structure are essential for music understanding tasks such as machine-assisted music co-writing, yet self-supervised approaches for symbolic music representation remain underexplored, particularly those that encode the hierarchical multiscale nature of musical structures.
arXiv:2608. 03920v1 Announce Type: cross Abstract: Humans recognize a musical passage even when it is shifted in time or transposed in pitch, indicating a notion of equivariance in the representation space.
arXiv:2606. 14612v1 Announce Type: cross Abstract: We show that the three movements of Beethoven's "Moonlight Sonata" (Op.
The paper explains why GPT‑style language models fail to transfer directly to symbolic music. It argues that success in language comes from tokenization that compresses data by creating a coordinate system where recurring patterns become predictable. For music, the authors propose that tokenization must build a predictively effective, relationally lossless coordinate system—defining Fact–Token and Token–State boundaries—to enable compression without sacrificing contextual freedom. Controlled experiments confirm that proper coordinate construction improves predictive compressibility, whereas mere sequence compaction does not.
ONOTE is a unified framework that treats music as a scientifically structured domain of measurable cross-representation correspondences, focusing on omnimodal notation processing centered on sheet music. It introduces a test-only benchmark drawing from diverse musical sources—including staff, Jianpu, and tablature—across varied genres, instruments, and structural conditions, with aligned multimodal derivatives. The framework supports four complementary tasks—score understanding, notation conversion, audio transcription, and symbolic generation—while constructing a provenance-bearing proposition hypergraph from external music-theory materials for evidence retrieval and deterministic validity checks.
arXiv:2607. 03806v1 Announce Type: cross Abstract: Audio foundation models are widely adopted as general-purpose feature extractors, yet the internal structure of their learned representations remains insufficiently understood.
arXiv:2608. 14819v1 Announce Type: cross Abstract: Music foundation models are commonly used as frozen audio feature extractors, yet selecting which layer to extract from remains largely heuristic.