Music Generation Evaluation

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4 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

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MIRA: A Musical Intent Refinement Agent for Aligning Text-to-Music Generation with User Intent
Oct 7, 2026Zekai Liu, Zhilin Wang, Xuzheng He +2Text-to-Music Generation Music Generation Evaluation
Shandong University · University of Science and Technology of China · Central Conservatory of Music +2
Tracing Inputs, Verifying Outputs: Validating Attribution in Music Generation
Oct 7, 2026Taejun Kim, Wonil Kim, Jongmin Jung +10Music Generation Evaluation
Neutune, Seoul, South Korea · KAIST, Daejeon, South Korea
Sobolev Norms in Neural Embeddings Measure Audio Morphing Regularity
Oct 6, 2026Théo Chasle Cauchy, Modan Tailleur, Barbara Pascal +2Music Generation Evaluation
Nantes Universit´e, ´Ecole Centrale Nantes, CNRS, LS2N, UMR6004, F-44000 Nantes, France. · Arturia, Montbonnot Saint-Martin, France
Adapting a Latent Audio Diffusion Model to Historical Guqin Recordings: A Listening-Driven Case Study
Oct 5, 2026Huanchen CaiText-to-Music Generation Music Generation Evaluation
Division of Speech, Music and Hearing (TMH) KTH Royal Institute of Technology, Stockholm, Sweden
Do Music Generative Models Understand Musical Qualities? Automatic Music Evaluation with Model-Intrinsic Signals
Sep 29, 2026Xiaosha Li, Chun Liu, Ziyu WangMusic Generation Evaluation Automated Evaluation
Georgia Institute of Technology · ByteDance Inc. · New York University · MBZUAI
From Human Narrative to Harmonic Structure: A Human-Centered Investigation of Algorithmic Music Generation through the Chord Wheel Diagram
Sep 28, 2026Josef Pavlíček, Petra Pavlíčková, Irena ŠtrausováMusic Generation Evaluation Symbolic Music Generation
Faculty of Information Technology, CTU, Thákurova 9, Prague 6, 160 00, Czech Republic · University of South Bohemia in České Budějovice, Faculty of Economics, Department of Applied Mathematics and Informatics, Branišovská 1645/31a, České Budějovice, Czech Republic
Do Language Models Need Music Supervision? Verifiable Rewards for Multi-Constraint Symbolic Music Generation
Sep 20, 2026Haoyue Liu, Xiaoying TangMusic Generation Evaluation Controllable Music Generation
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China · Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)
TTM-Bench: A Framework for Text-to-Music System Performance Benchmarking
Sep 16, 2026Giorgia Adorni, Michela Papandrea, Battista Rimoldi +1Text-to-Music Generation Music Generation Evaluation
Institute of Information Systems and Networking (ISIN), SUPSI, Lugano, Switzerland · t2b AG, Basel, Switzerland
Local Chord Corruption Is Not Recognizer Replay: Structure-Matched Calibration for Chord-Conditioned Generation
Sep 3, 2026Weiwen HuangMusic Generation Evaluation Controllable Music Generation
Shenzhen University
On the Human and Computer Alignment of Attribute-Based Music Matches
Sep 1, 2026Roser Batlle-Roca, Woosung Choi, Joan Serrà +5Music Generation Evaluation Music Information Retrieval
Music Technology Group, Universitat Pompeu Fabra · Sony AI · Joint Research Centre, European Commission +1
MusGU+: Toward a Musician-Centered Evaluation Framework and Discovery Tool for Generative Music AI
Aug 31, 2026Laura Ibáñez-Martínez, Roser Batlle-Roca, Xavier Serra +1Music Generation Evaluation Controllable Music Generation
Music Technology Group Universitat Pompeu Fabra Barcelona, Spain
How Well Do Generative Music Models Follow Emotion Conditioning?
Aug 30, 2026Morteza HeydariText-to-Music Generation Music Generation Evaluation
Istinye University, Graduate School, Istanbul, Turkey
Do Text-to-Music Models Really Follow Instructions? A Counterfactual Evaluation of Key and Beat Grouping
Aug 12, 2026Yining WangText-to-Music Generation Music Generation Evaluation
Bilibili Inc.
MuseCritic: Learning Multi-Aspect Song Rewards through Natural-Language Aesthetic Critiques
Aug 12, 2026Jiabao Zhuang, Changhao Jiang, Hanchen Wang +11Music Generation Evaluation Reward Modeling
Fudan NLP Group, Fudan University
InvFlowFD: Reference-Free and Background-Set-Free Perceptual Music Quality Metric with Flow Matching Inversion
Aug 4, 2026Alon Ziv, Harel Pogoda, Yossi AdiMusic Generation Evaluation Music Generation
School of Computer Science and Engineering The Hebrew University of Jerusalem, Israel
Agogic: Performance-Timed Music Tokens for LLM-Native Text-to-Symbolic-Music Generation
Aug 4, 2026Junhao Chen, Mingjin Chen, Jingjia Mao +12Text-to-Music Generation Music Generation Evaluation
Tsinghua University · The Hong Kong Polytechnic University · Beijing Technology and Business University +7
A Diagnostic Evaluation Framework for AI-Generated Cover Songs Using Music-Theoretic and Acoustic Features
Jul 22, 2026Yingxin LiangMusic Generation Evaluation Music Generation
Hangzhou Xiaoying Innovation Technology Co., Ltd. (Rythmix AI)
Predicting Timbre Traits for Interpretable Assessment of Musical Sound Synthesizers
Jun 29, 2026Théo Chasle Cauchy, Modan Tailleur, Lindsey Reymore +2Music Generation Evaluation Music Information Retrieval
Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France · Arizona State University, School of Music, Dance and Theatre, Tempe, AZ, USA · Arturia, Montbonnot Saint-Martin, France
TuneJury: An Open Metric for Improving Music Generation Preference Alignment
Jun 15, 2026Yonghyun Kim, Junwon Lee, Haiwen Xia +5Text-to-Music Generation Music Generation Evaluation
Georgia Tech · KAIST · Peking University +3
DeRA-MOS: Optimizing Text-to-Music Evaluation via Decoupled Listwise Ranking and Modality Alignment
Jun 8, 2026Chien-Chun Wang, Hung-Shin Lee, Hsin-Min Wang +1Text-to-Music Generation Music Generation Evaluation
E.SUN Financial Holding Co., Ltd., Taipei 10546, Taiwan · United Link Co., Ltd., Taipei 11493, Taiwan · Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan +1
Can LLMs understand LilyPond? A benchmark for symbolic music generation and understanding
Jun 7, 2026Matteo Spanio, Mohammad Torabi, Andrea Poltronieri +1Music Generation Evaluation LLM Evaluation
Centro di Sonologia Computazionale, University of Padova, Padova, Italy · Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain
Exploring LLMs for South Asian Music Understanding and Generation
Jun 3, 2026Faria Binte Kader, Mohtasim Hadi Rafi, Shah Wasif Sajjad +1Music Generation Evaluation LLM Evaluation
University of Central Florida · Florida, USA · Auburn University +1
Academic Text-to-Music Grand Challenge: Datasets, Baselines, and Evaluation Methods
May 20, 2026Fang-Chih Hsieh, Wei-Jaw Lee, Chun-Ping Wang +3Text-to-Music Generation Music Generation Evaluation
Artificial Intelligence Center of Research Excellence, National Taiwan University, Taipei, Taiwan · Department of Performing Arts Technology, University of Michigan, Ann Arbor, MI, United States
Text2Score: Generating Sheet Music From Textual Prompts
May 13, 2026Keshav Bhandari, Sungkyun Chang, Abhinaba Roy +4Text-to-Music Generation Music Generation Evaluation
Queen Mary University of London, UK · EmotionWave, Korea · Singapore University of Technology and Design, Singapore +1
SongBench: A Fine-Grained Multi-Aspect Benchmark for Song Quality Assessment
Apr 16, 2026Dapeng Wu, Shun Lei, Wei Tan +5Text-to-Music Generation Music Generation Evaluation
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · Tencent
Evaluating Prompt Robustness in Text-to-Audio Systems for Adaptive Virtual Agents and Game Soundtracks
Mar 14, 2026Jiahui Wu, Mei SiMusic Generation Evaluation Text-to-Audio Generation
Rensselaer Polytechnic Institute Troy, New York, USA

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MIRA: A Musical Intent Refinement Agent for Aligning Text-to-Music Generation with User Intent

Zekai Liu, Zhilin Wang, Xuzheng He, Yu Cheng+1

Shandong University · University of Science and Technology of China · Central Conservatory of Music+2

Text-to-Music Generation Music Generation Evaluation Prompt Optimization

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Text-to-music systems produce increasingly convincing audio, yet evaluation reveals little about whether the result matches user intent. A global text-audio relevance score can overlook the implicit intent in underspecified prompts and mask failures in specific requirements, such as instrumentation, structure, rhythm, or mood progression. To bridge this gap, we formulate text-to-music intent alignment as satisfying a per-request rubric of independently verifiable items covering both a request's explicit requirements and its implied musical intent. Scoring items individually makes evaluation diagnostic by intent source and musical dimension, rather than a single opaque score. We instantiate this as MuRA-Bench, a benchmark of real-world platform requests curated by music experts. We further propose MIRA (Musical Intent Refinement Agent), a test-time agent that first grounds a request's intent into rubrics, then searches over prompt revisions for a black-box generator under a bounded budget, iteratively generating music, verifying it against the rubrics, and using this feedback to guide a trajectory-aware tree search. Experiments across open-source and commercial backends show that MIRA improves intent alignment, enabling an open-source generator to achieve performance comparable to representative commercial systems (e.g. Suno and Mureka). Project page: https://mirareview.github.io/.

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