The Ghost in the Keys: A Disklavier Demo for Human-AI Musical Co-Creativity
- URL: http://arxiv.org/abs/2511.01663v1
- Date: Mon, 03 Nov 2025 15:26:01 GMT
- Title: The Ghost in the Keys: A Disklavier Demo for Human-AI Musical Co-Creativity
- Authors: Louis Bradshaw, Alexander Spangher, Stella Biderman, Simon Colton,
- Abstract summary: Aria-Duet is an interactive system facilitating a real-time musical duet between a human pianist and Aria, a state-of-the-art generative model.<n>We analyze the system's output from a musicological perspective, finding the model can maintain stylistic semantics and develop coherent phrasal ideas.
- Score: 59.78509280246215
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: While generative models for music composition are increasingly capable, their adoption by musicians is hindered by text-prompting, an asynchronous workflow disconnected from the embodied, responsive nature of instrumental performance. To address this, we introduce Aria-Duet, an interactive system facilitating a real-time musical duet between a human pianist and Aria, a state-of-the-art generative model, using a Yamaha Disklavier as a shared physical interface. The framework enables a turn-taking collaboration: the user performs, signals a handover, and the model generates a coherent continuation performed acoustically on the piano. Beyond describing the technical architecture enabling this low-latency interaction, we analyze the system's output from a musicological perspective, finding the model can maintain stylistic semantics and develop coherent phrasal ideas, demonstrating that such embodied systems can engage in musically sophisticated dialogue and open a promising new path for human-AI co-creation.
Related papers
- Adaptive Accompaniment with ReaLchords [60.690020661819055]
We propose ReaLchords, an online generative model for improvising chord accompaniment to user melody.<n>We start with an online model pretrained by maximum likelihood, and use reinforcement learning to finetune the model for online use.
arXiv Detail & Related papers (2025-06-17T16:59:05Z) - Dialogue in Resonance: An Interactive Music Piece for Piano and Real-Time Automatic Transcription System [7.108713005834857]
Dialogue in Resonance> is an interactive music piece for a human pianist and a computer-controlled piano.<n>The computer interprets and responds to the human performer's input in real time, creating a musical dialogue.
arXiv Detail & Related papers (2025-05-22T05:50:13Z) - Apollo: An Interactive Environment for Generating Symbolic Musical Phrases using Corpus-based Style Imitation [5.649205001069577]
We introduce Apollo, an interactive music application for generating symbolic phrases of conventional western music.<n>The system makes it possible for music artists and researchers to generate new musical phrases in the style of the proposed corpus.<n>The generated symbolic music materials, encoded in the MIDI format, can be exported or streamed for various purposes.
arXiv Detail & Related papers (2025-04-18T19:53:51Z) - ReaLJam: Real-Time Human-AI Music Jamming with Reinforcement Learning-Tuned Transformers [53.63950017886757]
We introduce ReaLJam, an interface and protocol for live musical jamming sessions between a human and a Transformer-based AI agent trained with reinforcement learning.<n>We enable real-time interactions using the concept of anticipation, where the agent continually predicts how the performance will unfold and visually conveys its plan to the user.
arXiv Detail & Related papers (2025-02-28T17:42:58Z) - Musical Agent Systems: MACAT and MACataRT [6.349140286855134]
We introduce MACAT and MACataRT, two distinct musical agent systems crafted to enhance interactive music-making between human musicians and AI.<n>MaCAT is optimized for agent-led performance, employing real-time synthesis and self-listening to shape its output autonomously.<n>MacataRT provides a flexible environment for collaborative improvisation through audio mosaicing and sequence-based learning.
arXiv Detail & Related papers (2025-01-19T22:04:09Z) - MeLFusion: Synthesizing Music from Image and Language Cues using Diffusion Models [57.47799823804519]
We are inspired by how musicians compose music not just from a movie script, but also through visualizations.
We propose MeLFusion, a model that can effectively use cues from a textual description and the corresponding image to synthesize music.
Our exhaustive experimental evaluation suggests that adding visual information to the music synthesis pipeline significantly improves the quality of generated music.
arXiv Detail & Related papers (2024-06-07T06:38:59Z) - Interactive Melody Generation System for Enhancing the Creativity of
Musicians [0.0]
This study proposes a system designed to enumerate the process of collaborative composition among humans.
By integrating multiple Recurrent Neural Network (RNN) models, the system provides an experience akin to collaborating with several composers.
arXiv Detail & Related papers (2024-03-06T01:33:48Z) - Quantized GAN for Complex Music Generation from Dance Videos [48.196705493763986]
We present Dance2Music-GAN (D2M-GAN), a novel adversarial multi-modal framework that generates musical samples conditioned on dance videos.
Our proposed framework takes dance video frames and human body motion as input, and learns to generate music samples that plausibly accompany the corresponding input.
arXiv Detail & Related papers (2022-04-01T17:53:39Z) - Flat latent manifolds for music improvisation between human and machine [9.571383193449648]
We consider a music-generating algorithm as a counterpart to a human musician, in a setting where reciprocal improvisation is to lead to new experiences.
In the learned model, we generate novel musical sequences by quantification in latent space.
We provide empirical evidence for our method via a set of experiments on music and we deploy our model for an interactive jam session with a professional drummer.
arXiv Detail & Related papers (2022-02-23T09:00:17Z) - RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement
Learning [69.20460466735852]
This paper presents a deep reinforcement learning algorithm for online accompaniment generation.
The proposed algorithm is able to respond to the human part and generate a melodic, harmonic and diverse machine part.
arXiv Detail & Related papers (2020-02-08T03:53:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.