Construction of a Music Genre Preference Recognition Model Based on Deep Learning

Summary

This study built a deep learning model using EEG signals and music familiarity to predict music genre preference. Data were collected with a low‑cost Muse S device and analyzed using a CNN+RNN and an EEGNet model. Familiarity significantly improved accuracy, reaching up to 99%, showing its substantial value in preference prediction.

Abstract

With the increasing prevalence of mental health issues, music therapy has gained attention as a non-pharmacological intervention, and deep learning techniques have shown promise in music emotion recognition and preference prediction. This study constructed a deep neural network model (CNN+RNN/EEGNet) to efficiently identify music type preferences and examine the influence of user familiarity on prediction accuracy. EEG signals were collected using a four-channel Muse S wearable device, and user familiarity scores were used as input features. The study followed a four-stage workflow: preparation, experimental design, model construction, and result analysis. In the experimental design, music was categorized into rock, ballad, and folk, and EEG data and familiarity ratings were collected for each category. Data was trained and tested using CNN+RNN or EEGNet models, and model performance was evaluated via subject-level 10-fold cross-validation. Results indicated that predicting all music types with EEG data alone achieved an accuracy of 82.28 ± 3.42%. For individual music types, accuracies were 91.13 ± 3.60% (rock), 91.83 ± 2.07% (ballad), and 87.87 ± 4.76% (folk). When incorporating user familiarity as a feature and using a multi-level rating output, overall prediction accuracy increased to 94.94 ± 1.61%, while individual music type accuracies reached 99.15 ± 1.56% (rock), 98.51 ± 2.30% (ballad), and 98.21 ± 2.60% (folk). These results demonstrate that combining familiarity features with a multi-level scoring system significantly improves the prediction of music preferences. By using an affordable, wearable Muse S EEG device and leveraging user familiarity, this study successfully developed a highly effective deep neural network model (CNN+RNN/EEGNet) for recognizing music type preferences. The findings indicate that both overall and individual music-type predictions benefit from the inclusion of familiarity information, highlighting the potential of this approach for personalized music recommendations and music therapy applications.

Introduction

Music plays a significant role in human life, and the widespread use of online platforms has made listening to music a routine daily activity. Beyond entertainment, music functions as a therapeutic tool that promotes relaxation, reduces pain, and supports individuals with developmental conditions. Musical preference directly influences both therapeutic outcomes and emotional responses. Previous studies show that preferred music enhances physical performance, such as endurance and sprint capacity¹, and reduces stress and pain². It is also an important intervention for older adults, in which individual preferences influenc....

Protocol

This study was conducted in accordance with the guidelines of the Ethics Committee of Quzhou University. The research protocol was approved by the Quzhou University Institutional Review Board (IRB) under approval number QZU-IRB-2026-037. All participants provided informed consent prior to their inclusion in the study.

Experimental preparation and participant recruitment
The experiment consisted of four stages (Figure 1): preparation, experimental design, model construction, and result analysis. In the preparation stage, research objectives were defined based on a review o....

Results

Model performance was evaluated under two conditions: (1) EEG features only and (2) EEG features combined with music familiarity. Classification was conducted for both combined music categories and individual music categories (rock, lyrical, and folk).

Table 3 presents results obtained using EEG features only, while Table 4 presents results obtained using EEG features combined with familiarity. Both CNN+RNN and EEGNet models were evaluated under these conditions, and EEGNet wa.......

Discussion

Previous studies on EEG-based music preference prediction have largely relied on multi-channel systems, limiting practical application^{21,22,23,24,25,26}. In this study, a four-channel wearable EEG device (Muse S) achieved comparable performance when combined with deep learning models and familiarity features, demonstrating a m.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Alcohol wipes	Any standard supplier	N/A	Used for cleaning headphones between participants
Data acquisition computer	Any standard manufacturer	N/A	Runs Muse SDK and Lab Streaming Layer (LSL)
EEG headset (Muse S)	InteraXon Inc.	https://choosemuse.com/muse-s/	Four-channel wearable EEG device (TP9, AF7, AF8, TP10)
Final E3000 in-ear headphones	Final Audio Design	https://snext-final.com/en/products/detail/E3000	Used for audio playback during experiment
Lab Streaming Layer (LSL) software	Open-source	https://github.com/sccn/labstreaminglayer	Used for EEG data streaming
Muse SDK	InteraXon Inc.	https://developer.choosemuse.com/	Used for EEG data acquisition
Questionnaire Star (online platform)	Changsha Ranxing Information Technology Co., Ltd.	https://www.wjx.cn/	Used for familiarity data collection
Python 3.10	Python Software Foundation	https://www.python.org/downloads/	Used for data processing and analysis
TensorFlow 2.12	Google	https://www.tensorflow.org/	Deep learning framework
Keras 2.12	Google	https://keras.io/	Neural network API
MNE-Python 1.3	Open-source	https://mne.tools/stable/index.html	EEG data analysis
NumPy 1.26	Open-source	https://numpy.org/	Numerical computation
Pandas 2.1	Open-source	https://pandas.pydata.org/	Data manipulation