In this paper, we present a personalized music recommendation system (PMRS) based on the convolutional neural networks (CNN) approach. The CNN approach classifies music based on the audio signal beats of the music into different genres. In PMRS, we propose a collaborative filtering (CF) recommendation algorithm to combine the output of the CNN with the log files to recommend music to the user. The log file contains the history of all users who use the PMRS. The PMRS extracts the user’s history from the log file and recommends music under each genre. We use the million song dataset (MSD) to evaluate the PMRS. To show the working of the PMRS, we developed a mobile application (an Android version). We used the confidence score metrics for different music genre to check the performance of the PMRS.