arXiv:2607. 08756v1 Announce Type: cross Abstract: We present MulTTiPop, a dataset of pop music segments and their associated multitrack MIDI recordings for the evaluation of automatic music transcription models.
We present MulTTiPop, a dataset of pop music segments and their associated multitrack MIDI recordings for the evaluation of automatic music transcription models. MulTTiPop contains 572 segments of popular music totaling 3.
arXiv:2608. 06165v1 Announce Type: cross Abstract: Existing audio-to-score (A2S) systems primarily focus on classical music, and the application to popular music remains underexplored.
arXiv:2606. 26451v1 Announce Type: cross Abstract: Automatic singing quality assessment (SQA) requires evaluating lyrical correctness and musical fidelity while handling expressive variations.
arXiv:2506. 14293v4 Announce Type: replace-cross Abstract: We present Sleeping-DISCO 9M, a large-scale pre-training dataset for music and song.
arXiv:2606. 01686v1 Announce Type: cross Abstract: As generative platforms such as Suno and Udio reach human-grade audio quality, the scope of AI's utility has expanded across the entire music production workflow.
arXiv:2606. 12282v1 Announce Type: cross Abstract: Expressive performance rendering (EPR) aims to generate realistic performances constrained on sequences of notes.
arXiv:2606. 02638v1 Announce Type: cross Abstract: Recent advances in neural song generation have enabled high-quality synthesis from lyrics and global textual prompts.
As generative platforms such as Suno and Udio reach human-grade audio quality, the scope of AI's utility has expanded across the entire music production workflow. Beyond simple track generation, these advancements have catalyzed the adoption of AI-driven methodologies in diverse forms.
arXiv:2603. 23667v2 Announce Type: replace-cross Abstract: We introduce Echoes, a new dataset for music deepfake detection designed for training and benchmarking detectors under realistic and provider-diverse conditions.
arXiv:2607. 08168v1 Announce Type: cross Abstract: Existing methods for automatic music transcription are often limited to single-instrument recordings or fail on complex, real music mixes.
arXiv:2606. 16612v1 Announce Type: cross Abstract: The rapid advancement of AI music generators highlights the urgent need for reliable Synthetic Song Detection (SSD).
arXiv:2607. 27909v1 Announce Type: cross Abstract: Objective evaluation of expressive MIDI piano performances typically relies on attribute statistics such as timing, velocity, and duration of individual notes.