Dialogue in Resonance: An Interactive Music Piece for Piano and Real-Time Automatic Transcription System

• URL: http://arxiv.org/abs/2505.16259v1
• Date: Thu, 22 May 2025 05:50:13 GMT
• Title: Dialogue in Resonance: An Interactive Music Piece for Piano and Real-Time Automatic Transcription System
• Abstract summary: 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.
• Score: 7.108713005834857
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper presents <Dialogue in Resonance>, an interactive music piece for a human pianist and a computer-controlled piano that integrates real-time automatic music transcription into a score-driven framework. Unlike previous approaches that primarily focus on improvisation-based interactions, our work establishes a balanced framework that combines composed structure with dynamic interaction. Through real-time automatic transcription as its core mechanism, the computer interprets and responds to the human performer's input in real time, creating a musical dialogue that balances compositional intent with live interaction while incorporating elements of unpredictability. In this paper, we present the development process from composition to premiere performance, including technical implementation, rehearsal process, and performance considerations.

Related papers

• InterSing: Explicit Interaction Dynamics for 3D Duet Singing Animation and Beyond [56.764269093059504]
  InterSing is a framework for generating realistic 3D head animations for duet singing performances.<n>InterSing generates realistic and expressive singing head animations with stronger coordination and musical alignment than existing methods.
  arXiv  Detail & Related papers  (2026-09-04T09:02:52Z)
• MusiChat: Vibe Composing for Music Creation [0.0]
  MusiChat is a conversational vibe composing system that enables collaborative human-AI music creation.<n>MusiChat integrates a large language model with a hybrid symbolic music engine through a memory-augmented architecture.<n>We evaluate MusiChat through objective analysis and human studies, achieving 95.31% and 100% accuracy for single- and multi-turn interactions.
  arXiv  Detail & Related papers  (2026-07-27T03:51:40Z)
• From Textural Counterpoint to Feature Encoding: A Multi-Dimensional Machine Representation Study of Haydn's "The Lark" Integrating Electroacoustic Analysis [11.369230577047423]
  This paper conducts an interdisciplinary analysis of Haydn's String Quartet in D Major, The Lark.<n>The study first utilizes auditory analysis to dissect the counterpoint morphology of the leading voice.<n>The paper introduces a fundamentally new approach to low-level computer feature extraction.
  arXiv  Detail & Related papers  (2026-07-07T06:58:46Z)
• LK Jam: System Architecture and Implementation of a Real-Time Human-AI Interactive Music Generation System using Role-Aware GRU [5.407906676682284]
  LK_Jam is a real-time, bidirectional human-computer interactive music generation system.<n>It is based on a lightweight Gated Recurrent Unit (GRU) and a high-performance audio host architecture.
  arXiv  Detail & Related papers  (2026-06-19T01:03:44Z)
• Co-policy: Responsive Human-Robot Co-Creation for Musical Performances [58.92814200971293]
  Co-policy is a framework for human-robot musical co-creation.<n>It separates semantic intent grounding, constrained musical variation, and visuomotor execution.<n>We show improved intent alignment, execution accuracy, and response frequency over diffusion-policy and ablated baselines.
  arXiv  Detail & Related papers  (2026-06-18T08:08:16Z)
• Music of Changing Lines: Toward a Culturally Situated Approach to the I-Ching [78.66052394412223]
  Music of Changing Lines re-centers the I-Ching as a meaning-bearing framework rather than a neutral randomizer.<n>Users perform Wen Wang Fa coin casting, which is accompanied in real time through probabilistic musical processes.<n>The resulting hexagrams and changing lines are interpreted by a large language model, Gemini, in relation to the user's inquiry.
  arXiv  Detail & Related papers  (2026-05-19T18:35:47Z)
• PianoFlow: Music-Aware Streaming Piano Motion Generation with Bimanual Coordination [31.224933689911186]
  Bimanual piano motion generation requires precise modeling of complex musical structures and dynamic cross-hand coordination.<n>We propose PianoFlow, a flow-matching framework for precise and coordinated bimanual piano motion synthesis.<n>Our approach strategically leverages MIDI as a privileged modality during training, distilling these structured musical priors to achieve deep semantic understanding.
  arXiv  Detail & Related papers  (2026-04-14T15:07:21Z)
• U-Mind: A Unified Framework for Real-Time Multimodal Interaction with Audiovisual Generation [48.6868174403074]
  We introduce U-Mind, the first unified system for high-intelligence multimodal dialogue.<n>It supports real-time generation and jointly models language, speech, motion, and video synthesis within a single interactive loop.<n>We show that U-Mind achieves state-of-the-art performance on a range of multimodal interaction tasks.
  arXiv  Detail & Related papers  (2026-02-27T07:07:02Z)
• The Ghost in the Keys: A Disklavier Demo for Human-AI Musical Co-Creativity [59.78509280246215]
  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.
  arXiv  Detail & Related papers  (2025-11-03T15:26:01Z)
• EditGen: Harnessing Cross-Attention Control for Instruction-Based Auto-Regressive Audio Editing [54.10773655199149]
  We investigate leveraging cross-attention control for efficient audio editing within auto-regressive models.<n>Inspired by image editing methodologies, we develop a Prompt-to-Prompt-like approach that guides edits through cross and self-attention mechanisms.
  arXiv  Detail & Related papers  (2025-07-15T08:44:11Z)
• A Real-Time Gesture-Based Control Framework [2.432598153985671]
  We introduce a real-time, human-in-the-loop gesture control framework.<n>It can dynamically adapt audio and music based on human movement.<n>System is designed for live performances, interactive installations, and personal use.
  arXiv  Detail & Related papers  (2025-04-28T03:57:28Z)
• 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)
• 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)
• Revisiting Conversation Discourse for Dialogue Disentanglement [88.3386821205896]
  We propose enhancing dialogue disentanglement by taking full advantage of the dialogue discourse characteristics. We develop a structure-aware framework to integrate the rich structural features for better modeling the conversational semantic context. Our work has great potential to facilitate broader multi-party multi-thread dialogue applications.
  arXiv  Detail & Related papers  (2023-06-06T19:17:47Z)
• Channel-aware Decoupling Network for Multi-turn Dialogue Comprehension [81.47133615169203]
  We propose compositional learning for holistic interaction across utterances beyond the sequential contextualization from PrLMs. We employ domain-adaptive training strategies to help the model adapt to the dialogue domains. Experimental results show that our method substantially boosts the strong PrLM baselines in four public benchmark datasets.
  arXiv  Detail & Related papers  (2023-01-10T13:18:25Z)
• Towards Context-Aware Neural Performance-Score Synchronisation [2.0305676256390934]
  Music synchronisation provides a way to navigate among multiple representations of music in a unified manner. Traditional synchronisation methods compute alignment using knowledge-driven and performance analysis approaches. This PhD furthers the development of performance-score synchronisation research by proposing data-driven, context-aware alignment approaches.
  arXiv  Detail & Related papers  (2022-05-31T16:45:25Z)
• 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)
• Sequence Generation using Deep Recurrent Networks and Embeddings: A study case in music [69.2737664640826]
  This paper evaluates different types of memory mechanisms (memory cells) and analyses their performance in the field of music composition. A set of quantitative metrics is presented to evaluate the performance of the proposed architecture automatically.
  arXiv  Detail & Related papers  (2020-12-02T14:19:19Z)
• 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.