Neuroscience and Music Perception

Latest papers

• The Machine's Internal Clock: Do LLMs Share Human Temporal Illusions?

  Catherine Bao, Vivek Srikumar · 18 August 2026

  Human perception of time is subjective. Well-documented temporal illusions show that the brain relies on context and relational cues for judging duration instead of tracking elapsed time directly. Prior studies established these effects with visual and auditory stimuli. Existing LLM evaluations of t…

• Oscillatory Hierarchical Reservoirs for Human-like Rhythm Perception and Anticipation

  Zhongju Yuan, Geraint Wiggins, Dick Botteldooren · 4 August 2026

  Rhythm is a fundamental aspect of human behaviour, present from infancy and deeply embedded in cultural practices. Rhythm anticipation often occurs before surface event onsets, yet most neuroscience and artificial intelligence studies focus on metronome-based tasks, with less attention to complex mu…

• Perceptually Aligning Representations of Music via Noise-Augmented Autoencoders

  Mathias Rose Bjare, Giorgia Cantisani, Marco Pasini, Stefan Lattner, Gerhard Widmer · 8 July 2026

  We argue that training autoencoders to reconstruct inputs from noised versions of their encodings, when combined with perceptually motivated losses, yields encodings that are structured according to a perceptual hierarchy. We demonstrate the emergence of this hierarchy by showing that, after trainin…

• Moonlight in Latent Space: Chirality and Structural Correspondence Between Beethoven's Op. 27 No. 2 and Machine Learning Mechanisms

  Chen Ying Claude, Zhihan Luo · 15 June 2026

  We show that the three movements of Beethoven's "Moonlight Sonata" (Op. 27 No. 2) instantiate three distinct machine learning architectures -- not by analogy, but by structural correspondence. Through computational analysis of the score (entropy, Jensen-Shannon divergence, dissonance, hand distribut…

• Entropy as a Structural Prior: How a Log-Barrier on DiT Belief Space Drives Musical Diversity and Development

  Zixi Li, Youzhen Li · 8 June 2026

  Confidence-based loss weighting is usually avoided in generative models because it accelerates errors when the model is confidently wrong, but this intuition breaks down in supervised diffusion training. We introduce the Eisbach log-barrier, a parameter-free weight derived from the entropy of the Di…

• A Minimalist Brain-Computer Musical Interface for Real-Time Emotion-Driven Sonification: System Design and Preliminary Evaluation

  Pablo A. Monroy-D'Croz, Rafael Ramirez-Melendez, Julian Cespedes-Guevara · 2 June 2026

  This paper presents a minimalist brain-computer Musical Interface (BCMI) that functions as a real-time affective sonification system, translating prefrontal EEG activity into adaptive music. Emotional valence is estimated from frontal alpha asymmetry (AF7/AF8) and mapped to musical features such as …

• Teachers' Vocal Expressions and Student Engagement in Asynchronous Video Learning

  Hung-Yue Suen, Yu-Sheng Su · 19 May 2026

  Asynchronous video learning, including massive open online courses (MOOCs), offers flexibility but often lacks students' affective engagement. This study examines how teachers' verbal and nonverbal vocal emotive expressions influence students' self-reported affective engagement. Using computational …

• Music Interpretation and Emotion Perception: A Computational and Neurophysiological Investigation

  Vassilis Lyberatos, Spyridon Kantarelis, Ioanna Zioga, Christina Anagnostopoulou, Giorgos Stamou, Anastasia Georgaki · 6 May 2026

  This study investigates emotional expression and perception in music performance using computational and neurophysiological methods. The influence of different performance settings, such as repertoire, diatonic modal etudes, and improvisation, as well as levels of expressiveness, on performers' emot…

• ONOTE: Benchmarking Omnimodal Notation Processing for Expert-level Music Intelligence

  Menghe Ma, Siqing Wei, Yuecheng Xing, Yaheng Wang, Fanhong Meng, Peijun Han, Luu Anh Tuan, Haoran Luo · 24 April 2026

  Omnimodal Notation Processing (ONP) represents a unique frontier for omnimodal AI due to the rigorous, multi-dimensional alignment required across auditory, visual, and symbolic domains. Current research remains fragmented, focusing on isolated transcription tasks that fail to bridge the gap between…

• When Scaling Fails: Mitigating Audio Perception Decay of LALMs via Multi-Step Perception-Aware Reasoning

  Ruixiang Mao, Xiangnan Ma, Dan Chen, Ziming Zhu, Yuan Ge, Aokai Hao, Haishu Zhao, Yifu Huo, Qing Yang, Kaiyan Chang, Xiaoqian Liu, Chenglong Wang, Qiaozhi He, Tong Xiao, Jingbo Zhu · 4 March 2026

  Test-Time Scaling has shown notable efficacy in addressing complex problems through scaling inference compute. However, within Large Audio-Language Models (LALMs), an unintuitive phenomenon exists: post-training models for structured reasoning trajectories results in marginal or even negative gains …

• Expectation and Acoustic Neural Network Representations Enhance Music Identification from Brain Activity

  Shogo Noguchi, Taketo Akama, Tai Nakamura, Shun Minamikawa, Natalia Polouliakh · 4 March 2026

  During music listening, cortical activity encodes both acoustic and expectation-related information. Prior work has shown that ANN representations resemble cortical representations and can serve as supervisory signals for EEG recognition. Here we show that distinguishing acoustic and expectation-rel…

• Stochastic Parroting in Temporal Attention -- Regulating the Diagonal Sink

  Victoria Hankemeier, Malte Hankemeier · 12 February 2026

  Spatio-temporal models analyze spatial structures and temporal dynamics, which makes them prone to information degeneration among space and time. Prior literature has demonstrated that over-squashing in causal attention or temporal convolutions creates a bias on the first tokens. To analyze whether …

• Do Models Hear Like Us? Probing the Representational Alignment of Audio LLMs and Naturalistic EEG

  Haoyun Yang, Xin Xiao, Jiang Zhong, Yu Tian, Dong Xiaohua, Yu Mao, Hao Wu, Kaiwen Wei · 26 January 2026

  Audio Large Language Models (Audio LLMs) have demonstrated strong capabilities in integrating speech perception with language understanding. However, whether their internal representations align with human neural dynamics during naturalistic listening remains largely unexplored. In this work, we sys…

• SonicBench: Dissecting the Physical Perception Bottleneck in Large Audio Language Models

  Yirong Sun, Yanjun Chen, Xin Qiu, Gang Zhang, Hongyu Chen, Daokuan Wu, Chengming Li, Min Yang, Dawei Zhu, Wei Zhang, Xiaoyu Shen · 19 January 2026

  Large Audio Language Models (LALMs) excel at semantic and paralinguistic tasks, yet their ability to perceive the fundamental physical attributes of audio such as pitch, loudness, and spatial location remains under-explored. To bridge this gap, we introduce SonicBench, a psychophysically grounded be…

• Decoding Selective Auditory Attention to Musical Elements in Ecologically Valid Music Listening

  Taketo Akama, Zhuohao Zhang, Tsukasa Nagashima, Takagi Yutaka, Shun Minamikawa, Natalia Polouliakh · 8 December 2025

  Art has long played a profound role in shaping human emotion, cognition, and behavior. While visual arts such as painting and architecture have been studied through eye tracking, revealing distinct gaze patterns between experts and novices, analogous methods for auditory art forms remain underdevelo…

• A Convolutional Framework for Mapping Imagined Auditory MEG into Listened Brain Responses

  Maryam Maghsoudi, Mohsen Rezaeizadeh, Shihab Shamma · 4 December 2025

  Decoding imagined speech engages complex neural processes that are difficult to interpret due to uncertainty in timing and the limited availability of imagined-response datasets. In this study, we present a Magnetoencephalography (MEG) dataset collected from trained musicians as they imagined and li…

• Perception of AI-Generated Music - The Role of Composer Identity, Personality Traits, Music Preferences, and Perceived Humanness

  David Stammer, Hannah Strauss, Peter Knees · 3 December 2025

  The rapid rise of AI-generated art has sparked debate about potential biases in how audiences perceive and evaluate such works. This study investigates how composer information and listener characteristics shape the perception of AI-generated music, adopting a mixed-method approach. Using a diverse …

• Better audio representations are more brain-like: linking model-brain alignment with performance in downstream auditory tasks

  Leonardo Pepino, Pablo Riera, Juan Kamienkowski, Luciana Ferrer · 24 November 2025

  Artificial neural networks (ANNs) are increasingly powerful models of brain computation, yet it remains unclear whether improving their task performance also makes their internal representations more similar to brain signals. To address this question in the auditory domain, we quantified the alignme…

• Unique Hard Attention: A Tale of Two Sides

  Selim Jerad, Anej Svete, Jiaoda Li, Ryan Cotterell · 14 November 2025

  Understanding the expressive power of transformers has recently attracted attention, as it offers insights into their abilities and limitations. Many studies analyze unique hard attention transformers, where attention selects a single position that maximizes the attention scores. When multiple posit…

• Representing Classical Compositions through Implication-Realization Temporal-Gestalt Graphs

  A. V. Bomediano, R. J. Conanan, L. D. Santuyo, A. Coronel · 3 November 2025

  Understanding the structural and cognitive underpinnings of musical compositions remains a key challenge in music theory and computational musicology. While traditional methods focus on harmony and rhythm, cognitive models such as the Implication-Realization (I-R) model and Temporal Gestalt theory o…

• Exploring the correlation between the type of music and the emotions evoked: A study using subjective questionnaires and EEG

  Jelizaveta Jankowska, Bożena Kostek, Fernando Alonso-Fernandez, Prayag Tiwari · 31 October 2025

  The subject of this work is to check how different types of music affect human emotions. While listening to music, a subjective survey and brain activity measurements were carried out using an EEG helmet. The aim is to demonstrate the impact of different music genres on emotions. The research involv…