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Predicting music preference using deep learning on electroencephalogram and audio features

뇌파 및 오디오 특징을 활용한 딥러닝 모델 기반 음악 선호도 예측

Seunghyun Cho1
,
Hyunjae Kim2
,
Kyung Myun Lee23*
조 승현1
,
김 현재2
,
이 경면23*
1한국과학기술원 전기및전자공학부
2한국과학기술원 문화기술대학원
3한국과학기술원 디지털인문사회과학부
*Corresponding Author
31 July 2026. pp. 362-373
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Abstract

In this paper, we aimed to verify the practical contribution of Electroencephalography (EEG) information in music preference prediction. To this end, Null Electroencephalogram (Null EEG), in which temporal information was disrupted, was used as a control condition, and a controlled comparison between EEG and Null EEG was conducted using the same model architecture. The results showed that the EEG based model showed consistent performance improvements compared to Null EEG, confirming that EEG signals substantially contribute to music preference prediction. Furthermore, in comparison with the acoustic feature based model, the EEG only model demonstrated higher performance, while the model combining EEG and acoustic features achieved the best overall performance. Finally, an Region of Interest (ROI) ablation experiment was conducted to explore the contribution of different brain regions to music preference prediction, revealing that the occipital and frontal regions were strongly associated with music preference prediction. Overall, this study demonstrates the role of EEG information in predicting individual music preferences and suggests the potential of its complementary use with acoustic features.

Keywords
Music preference prediction
Electroencephalogram (EEG)
Audio features
Brain region analysis

본 논문은 음악 선호도 예측에서 뇌파(Electroencephalogram, EEG) 정보의 실질적 기여를 검증하고자 하였다. 이를 위해 시간적 정보가 교란된 대조뇌파(Null Electroencephalogram, Null EEG) 데이터를 대조군으로 설정하고, 동일한 모델 구조에서 뇌파와 통제된 비교 실험을 수행하였다. 그 결과 뇌파 기반 모델은 대조뇌파 대비 일관된 성능 향상이 관찰되었으며, 음악 선호도 예측에 있어 뇌파 신호가 실질적으로 기여하는 것을 확인하였다. 이어서 오디오 특징 기반 모델과의 비교에서 뇌파 단독 모델이 오디오 특징 기반 모델에 비해 더 높은 성능을 보였으며, 뇌파와 오디오 특징을 결합한 모델이 최고 성능을 달성하였다. 마지막으로 관심영역(Region of Interest, ROI) 절제 실험을 통해 뇌 영역별 음악 선호도 예측 기여도를 탐색하였고 후두부와 전두엽 영역이 음악 선호도 예측과 큰 연관이 있음을 보였다. 본 연구는 개인의 음악 선호도 예측에서 뇌파의 역할과 오디오 특징과의 상호보완적 활용 가능성을 제시한다.

키워드
음악 선호도 예측
뇌파(Electroencephalogram, EEG)
오디오 특징
뇌 영역 분석
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Information
  • Publisher :The Acoustical Society of Korea
  • Publisher(Ko) :한국음향학회
  • Journal Title :The Journal of the Acoustical Society of Korea
  • Journal Title(Ko) :한국음향학회지
  • Volume : 45
  • No :4
  • Pages :362-373
  • Received Date : 2026-06-01
  • Accepted Date : 2026-07-02
  • DOI :https://doi.org/10.7776/ASK.2026.45.4.362
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