arXiv AI
Jul 29
MusiChat: Vibe Composing for Music Creation
arXiv:2607. 24873v1 Announce Type: new Abstract: Recent advances in AI music generation have enabled users to create complete musical pieces from natural-language prompts.
Musical Agent Systems: MACAT and MACataRT
arXiv:2502. 00023v2 Announce Type: replace-cross Abstract: Our research explores the development and application of musical agents, human-in-the-loop generative AI systems designed to support music performance and improvisation within co-creative spaces.
Related stories
Hugging Face Trending Papers
Jul 27
MusiChat: Vibe Composing for Music Creation
Recent advances in AI music generation have enabled users to create complete musical pieces from natural-language prompts. However, most existing systems follow a prompt-and-regenerate paradigm, making iterative refinement difficult because users must repeatedly recreate compositions instead of directly evolving existing musical ideas.
arXiv AI
Jun 24
Real-Time Interactive Music Generation via Data-Free Streaming Consistency Distillation
arXiv:2606. 24307v1 Announce Type: cross Abstract: Interactive music and live performance relies on real-time human expression, but modern generative music AI remains largely absent from this domain due to its prohibitive inference latency and offline rendering paradigm.
arXiv AI
Sep 1
MusGU+: Toward a Musician-Centered Evaluation Framework and Discovery Tool for Generative Music AI
arXiv:2608.30940v1 Announce Type: cross Abstract: Generative music systems are increasingly presented as tools that democratize music creation, yet their practical suitability for musicians remains u...
arXiv AI
Jun 26
Pianist Transformer: Towards Expressive Piano Performance Rendering via Scalable Self-Supervised Pre-Training
arXiv:2512. 02652v2 Announce Type: replace-cross Abstract: Existing methods for expressive music performance rendering, a conditional generation task that aims to generate a human-like performance from a symbolic score, rely on supervised learning over small labeled datasets, which limits scaling of both data volume and model size, despite the availability of vast unlabeled music, as in vision and language.
arXiv AI
Jun 19
Co-policy: Responsive Human-Robot Co-Creation for Musical Performances
arXiv:2606. 19914v1 Announce Type: cross Abstract: Art has long stood as a pivotal expression of human creativity.
Hugging Face Trending Papers
Jun 25
DanceDuo: Bridging Human Movement and AI Choreography
In recent years, advancements in deep learning and generative models have revolutionized music-driven dance generation. This paper introduces a novel platform, namely DanceDuo, leveraging diffusion models to generate AI-choreographed dance sequences synchronized with a variety of music genres, to encourage dancing practice.
Hugging Face Trending Papers
Jun 1
HAIM: Human-AI Music Datasets for AI Music Production Tracking Benchmark
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 AI
Jun 2
HAIM: Human-AI Music Datasets for AI Music Production Tracking Benchmark
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 AI
Aug 12
MazzikaAI: A knowledge-based performance-to-prompt compiler for real-time Arabic maqam accompaniment with a streaming text-to-music model
arXiv:2608. 10360v1 Announce Type: cross Abstract: Arabic maqam music microtonal, modal, and built on ornamented call and response is among the traditions most underserved by generative music models, whose training frameworks remain predominantly Western and equaltempered.
arXiv Machine Learning
Aug 6
Helping Music Co-Creation Agents 'Listen' Well: Hierarchical Self-Supervised World Models for Understanding and Generation
arXiv:2608. 04378v1 Announce Type: cross Abstract: Collaborative music agents need internal representations rich enough to support both understanding and generation, yet flexible enough for a workflow where the human retains agency.
arXiv Machine Learning
Sep 14
Musical Attention Transformer: Music Generation Using a Music-Specific Attention Model
The paper introduces Musical Attention, a Transformer-based music generation model that incorporates meta-information such as bar numbers, key, signatures, and tempos into its attention mechanism. By representing each note with five events (pitch, bar number, onset, duration, velocity) plus three metadata elements, the model captures correlations among eight features, improving musical coherence and reducing repetition. Experiments show that Musical Attention outperforms prior methods like Full Attention and Strided Attention in coherence, variation, and overall quality, producing more diverse and harmonically consistent melodies.