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

• URL: http://arxiv.org/abs/2512.05528v1
• Date: Fri, 05 Dec 2025 08:39:36 GMT
• Title: Decoding Selective Auditory Attention to Musical Elements in Ecologically Valid Music Listening
• Authors: Taketo Akama, Zhuohao Zhang, Tsukasa Nagashima, Takagi Yutaka, Shun Minamikawa, Natalia Polouliakh,
• Abstract summary: This study is the first attempt to decode selective attention to musical elements using naturalistic, studio-produced songs and a lightweight consumer-grade EEG device with only four electrodes.<n>Our contributions are fourfold: (i) decoding music attention in real studio-produced songs, (ii) demonstrating feasibility with a four-channel consumer EEG, (iii) providing insights for music attention decoding, and (iv) demonstrating improved model ability over prior work.
• Score: 2.9306210138702142
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 underdeveloped. Music, despite being a pervasive component of modern life and culture, still lacks objective tools to quantify listeners' attention and perceptual focus during natural listening experiences. To our knowledge, this is the first attempt to decode selective attention to musical elements using naturalistic, studio-produced songs and a lightweight consumer-grade EEG device with only four electrodes. By analyzing neural responses during real world like music listening, we test whether decoding is feasible under conditions that minimize participant burden and preserve the authenticity of the musical experience. Our contributions are fourfold: (i) decoding music attention in real studio-produced songs, (ii) demonstrating feasibility with a four-channel consumer EEG, (iii) providing insights for music attention decoding, and (iv) demonstrating improved model ability over prior work. Our findings suggest that musical attention can be decoded not only for novel songs but also across new subjects, showing performance improvements compared to existing approaches under our tested conditions. These findings show that consumer-grade devices can reliably capture signals, and that neural decoding in music could be feasible in real-world settings. This paves the way for applications in education, personalized music technologies, and therapeutic interventions.

Related papers

• Music Flamingo: Scaling Music Understanding in Audio Language Models [98.94537017112704]
  Music Flamingo is a novel large audio-language model designed to advance music understanding in foundational audio models.<n> MF-Skills is a dataset labeled through a multi-stage pipeline that yields rich captions and question-answer pairs covering harmony, structure, timbre, lyrics, and cultural context.<n>We introduce a post-training recipe: we first cold-start with MF-Think, a novel chain-of-thought dataset grounded in music theory, followed by GRPO-based reinforcement learning with custom rewards.
  arXiv  Detail & Related papers  (2025-11-13T13:21:09Z)
• From Sound to Sight: Towards AI-authored Music Videos [6.8291397456847625]
  We propose two novel pipelines for automatically generating music videos from any user-specified, vocal or instrumental song.<n>Inspired by the manual of music video producers, we experiment on how well latent feature-based techniques can analyse audio.<n>Next, we employ a generative model to produce the corresponding video clips.
  arXiv  Detail & Related papers  (2025-08-20T13:54:53Z)
• A Survey of Foundation Models for Music Understanding [60.83532699497597]
  This work is one of the early reviews of the intersection of AI techniques and music understanding. We investigated, analyzed, and tested recent large-scale music foundation models in respect of their music comprehension abilities.
  arXiv  Detail & Related papers  (2024-09-15T03:34:14Z)
• Bridging Paintings and Music -- Exploring Emotion based Music Generation through Paintings [10.302353984541497]
  This research develops a model capable of generating music that resonates with the emotions depicted in visual arts. Addressing the scarcity of aligned art and music data, we curated the Emotion Painting Music dataset. Our dual-stage framework converts images to text descriptions of emotional content and then transforms these descriptions into music, facilitating efficient learning with minimal data.
  arXiv  Detail & Related papers  (2024-09-12T08:19:25Z)
• 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)
• Binaural SoundNet: Predicting Semantics, Depth and Motion with Binaural Sounds [118.54908665440826]
  Humans can robustly recognize and localize objects by using visual and/or auditory cues. This work develops an approach for scene understanding purely based on sounds. The co-existence of visual and audio cues is leveraged for supervision transfer.
  arXiv  Detail & Related papers  (2021-09-06T22:24:00Z)
• Musical Prosody-Driven Emotion Classification: Interpreting Vocalists Portrayal of Emotions Through Machine Learning [0.0]
  The role of musical prosody remains under-explored despite several studies demonstrating a strong connection between prosody and emotion. In this study, we restrict the input of traditional machine learning algorithms to the features of musical prosody. We utilize a methodology for individual data collection from vocalists, and personal ground truth labeling by the artist themselves.
  arXiv  Detail & Related papers  (2021-06-04T15:40:19Z)
• Music Harmony Generation, through Deep Learning and Using a Multi-Objective Evolutionary Algorithm [0.0]
  This paper introduces a genetic multi-objective evolutionary optimization algorithm for the generation of polyphonic music. One of the goals is the rules and regulations of music, which, along with the other two goals, including the scores of music experts and ordinary listeners, fits the cycle of evolution to get the most optimal response. The results show that the proposed method is able to generate difficult and pleasant pieces with desired styles and lengths, along with harmonic sounds that follow the grammar while attracting the listener, at the same time.
  arXiv  Detail & Related papers  (2021-02-16T05:05:54Z)
• Music Gesture for Visual Sound Separation [121.36275456396075]
  "Music Gesture" is a keypoint-based structured representation to explicitly model the body and finger movements of musicians when they perform music. We first adopt a context-aware graph network to integrate visual semantic context with body dynamics, and then apply an audio-visual fusion model to associate body movements with the corresponding audio signals.
  arXiv  Detail & Related papers  (2020-04-20T17:53:46Z)
• Multi-Modal Music Information Retrieval: Augmenting Audio-Analysis with Visual Computing for Improved Music Video Analysis [91.3755431537592]
  This thesis combines audio-analysis with computer vision to approach Music Information Retrieval (MIR) tasks from a multi-modal perspective. The main hypothesis of this work is based on the observation that certain expressive categories such as genre or theme can be recognized on the basis of the visual content alone. The experiments are conducted for three MIR tasks Artist Identification, Music Genre Classification and Cross-Genre Classification.
  arXiv  Detail & Related papers  (2020-02-01T17:57:14Z)

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.