OPTIMIZING ATHLETIC TRAINING: DEEP LEARNING-BASED MUSIC CONTENT IDENTIFICATION AND RECOMMENDATION SYSTEMS

Abstract

Objective: This study investigates a deep learning-based method for music content identification and recommendation, aimed at optimizing music selection in sports training environments. With the vast array of digital music available online, traditional methods for music recommendation are often insufficient to meet the diverse preferences of athletes, who may require specific types of music to enhance their training and performance. Methods: Utilizing a convolutional neural network (CNN), this research develops an algorithm tailored for the identification of music by genre, tempo, and rhythm, which are crucial for matching music to specific athletic activities. The system then recommends music tracks based on an athlete's historical preferences and the identified music characteristics that align with optimal training outcomes. Results: The proposed method was evaluated through a series of experiments that tested its ability to accurately identify diverse styles of music and effectively recommend tracks that meet the specific needs of athletes. The results indicate a high degree of accuracy in music style recognition and a significant improvement in user satisfaction with the recommended playlists, compared to traditional recommendation systems. Conclusion: The deep learning-based music recommendation system presents a substantial advancement for incorporating music into sports training regimes. By providing tailored music recommendations, the system not only meets the varied musical tastes of athletes but also supports enhanced performance through the strategic use of music. This technology has the potential to transform how music is integrated into athletic training, making it a valuable tool for coaches and trainers seeking to exploit the motivational and performance-enhancing effects of music.