Music Understanding

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24 papers7 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Does Notation Matter? MusicXML Tokenisation for Symbolic Music Generation and Understanding
Oct 25, 2026Andrea Poltronieri, Matteo Spanio, Simone Chieppa +2Symbolic Music Generation Tokenization in Language Models
Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain · University of Padova, Padova, Italy
Heard But Not Said: Prompting Understates Temporal Information of Musical Moments in Frozen Large Audio-Language Models
Oct 21, 2026Eunsik ShinAudio-Language Models Audio-Language Model Evaluation
Music and Audio Research Group (MARG), Department of Intelligence and Information, Seoul National University
Derivation Depth Exposes Reasoning Failures in Tool-Augmented Music Analysis
Oct 21, 2026Rupali Rajesh, Sethuraman T, Savya Khosla +4Music Understanding Audio-Language Model Evaluation
IIT Madras · UIUC · Google +2
MUB: An Identity-Masked Benchmark for Music Understanding in Multimodal Language Models
Oct 16, 2026Kun Liu, Lyra TaoAudio-Language Model Evaluation Music Understanding
STEMMA: Song-to-Stem Multi-Audio Reasoning for Large Audio Language Models
Oct 8, 2026Hoyeol Sohn, Wonil Kim, Keunhyoung Kim +7Audio Reasoning Music Understanding
Graduate School of Culture Technology, KAIST, Daejeon, Republic of Korea · Neutune, Seoul, Republic of Korea
SheetSage2: Coherent Lead-Sheet Transcription with Synthetic Supervision
Oct 4, 2026Junyan Jiang, Ruibin Yuan, Jiahao Pan +4Automatic Music Transcription Music Understanding
New York University · MBZUAI · ACE Studio +1
From Isolated Feature to Orbits: Discovering Music Concepts via Multi-SAE Alignment
Oct 1, 2026Liwei Lin, Gus XiaRepresentation Learning Sparse Autoencoders
Computer Science Department, NYU Shanghai · Machine Learning Department, MBZUAI
MUUNRiver-Bench: Diagnosing Relation-Dependent Music Retrieval with Multimodal Instructions
Sep 14, 2026Zhancheng Guo, Congren Dai, Shangda Wu +4Audio-Language Model Evaluation Audio-Text Retrieval
Central Conservatory of Music, Beijing, China · Tsinghua University, Beijing, China
ScorePrompts: Natural-Language Exploration of Symbolic Music Scores through Analysis
Sep 9, 2026Emmanouil Karystinaios, Gerhard WidmerMusic Information Retrieval Music Understanding
Institute of Computational Perception, Johannes Kepler University Linz, Austria
Note-Level Temporal Grounding of Musical Concepts in Large Audio-Language Models
Aug 30, 2026Kun Fang, Ziyu Wang, Ichiro FujinagaMusic Understanding Audio-Language Models
Schulich School of Music, McGill University, Montr´eal, QC, Canada · CIRMMT (Centre for Interdisciplinary Research in Music Media and Technology), Montr´eal, Canada · Courant Institute, New York University, New York, USA +1
MuSP-Bench: Advanced Multimodal Benchmarking of Music Understanding across Score and Performance
Aug 28, 2026Milan Liessens Dujardin, Song-Ze Yu, Kevin MiaoMusic Understanding Multimodal Model Evaluation
Bryel Labs · UC Berkeley
Dissonance Spectrum explicitly models perceptual frequency interactions for better music understanding
Aug 26, 2026Tianle Wang, Xinyi Tong, Liangke Zhao +7Music Understanding Time-Frequency Analysis
Beijing Institute for General Artificial Intelligence, Beijing, China · Central Conservatory of Music, Beijing, China · Peking University, Beijing, China
Music-JEPA: Learning a World Model of Sound from Action
Jul 24, 2026Ziyu Wang, Kun Fang, Yann LeCunAudio Representation Learning World Model Learning
Courant Institute, New York University · Schulich School of Music, McGill University · Centre for Interdisciplinary Research in Music Media and Technology (CIRMMT) +2
Beyond Binary Instrument QA: Probing Instrument Grounding in Music Audio-Language Models
Jun 30, 2026Yujun Lee, Joonhyeok Shin, Hyoeun Kim +1Audio-Language Model Evaluation Audio QA
Sungkyunkwan University
When the Same Musical Knowledge Forgets Differently: A Clean Probe of Pathway-Dependent Forgetting
Jun 13, 2026Yu Liu, Zhiwei Yang, Wenxiao Zhang +8Music Understanding Audio-Language Models
Institute of Information Engineering, CAS · School of Cyber Security, UCAS · The University of Western Australia +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
MusTBench: Benchmarking and Advancing Temporal Grounding in Music LLMs
May 28, 2026Daeyong Kwon, Qiyu Wu, Shinobu Kuriya +6Audio-Language Model Evaluation Music Understanding
1Seoul National University · 2Sony Group Corporation · 3Sony AI
MERIT: Learning Disentangled Music Representations for Audio Similarity
May 26, 2026Abhinaba Roy, Junyi Liang, Dorien HerremansDisentangled Representation Learning Audio Representation Learning
Singapore University of Technology and Design
PHALAR: Phasors for Learned Musical Audio Representations
May 5, 2026Davide Marincione, Michele Mancusi, Giorgio Strano +4Audio Representation Learning Representation Learning
Department of Computer Science, Sapienza University of Rome, Italy · Moises Systems, Inc. · Paradigma, Inc.
GaMMA: Towards Joint Global-Temporal Music Understanding in Large Multimodal Models
May 1, 2026Zuyao You, Zhesong Yu, Mingyu Liu +3Music Understanding Multimodal Pretraining
1Fudan University · 2ByteDance
TinyMU: A Compact Audio-Language Model for Music Understanding
Apr 17, 2026Xiquan Li, Aurian Quelennec, Slim EssidMusic Understanding Small Language Models
LTCI, T´el´ecom Paris, Institut Polytechnique de Paris · Shanghai Jiao Tong University
Jamendo-MT-QA: A Benchmark for Multi-Track Comparative Music Question Answering
Apr 8, 2026Junyoung Koh, Jaeyun Lee, Soo Yong Kim +5Audio QA Music Understanding
Yonsei University · KRAFTON · MODULABS MAAP +4
HumMusQA: A Human-written Music Understanding QA Benchmark Dataset
Mar 29, 2026Benno Weck, Pablo Puentes, Andrea Poltronieri +2Music Understanding Audio-Language Model Evaluation
Universitat Pompeu Fabra · Universitat Autònoma de Barcelona
Voices of Civilizations: A Multilingual QA Benchmark for Global Music Understanding
Feb 28, 2026Shangda Wu, Ziya Zhou, Yongyi Zang +4Music Understanding Audio-Language Model Evaluation
Independent Researcher · The Hong Kong University of Science and Technology, Hong Kong SAR, China · The Chinese University of Hong Kong, Hong Kong SAR, China

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Does Notation Matter? MusicXML Tokenisation for Symbolic Music Generation and Understanding

Andrea Poltronieri, Matteo Spanio, Simone Chieppa, Xavier Serra+1

Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain · University of Padova, Padova, Italy

Symbolic Music Generation Tokenization in Language Models Music Understanding Music Generation Evaluation

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Symbolic music representations encode musical content at varying levels of completeness, from performance-oriented formats such as MIDI to fully notated scores such as MusicXML. Yet the impact of this representational richness on deep learning models remains poorly understood. While tokenisation has been extensively studied for MIDI, score-based formats have received considerably less attention, and MusicXML in particular remains largely unexplored despite being the de-facto standard for score interchange. In this paper, we introduce the first general-purpose tokenisation framework for MusicXML, and conduct a systematic empirical study to determine whether notational richness confers a measurable advantage for deep learning on symbolic music. We train 26 autoregressive and masked language models, comparing MusicXML tokenisation against MIDI and ABC-based representations, probing the effect of different tokenisation strategies within MusicXML, and testing whether music-grammar-aware tokenisation outperforms treating scores as plain text. We evaluate music understanding tasks using linear probing across composer classification, difficulty estimation, and accompaniment suggestion, and generative tasks using objective statistical metrics and a human pairwise preference study. Our results show that MusicXML-tokenised models outperform MIDI and text-based counterparts on understanding and generative tasks.