Music Genre Recommendations Based on Spectrogram Analysis Using Convolutional Neural Network Algorithm with RESNET-50 and VGG-16 Architecture

Abstract

Recommendations are a very useful tool in many industries. Recommendations provide the best selection of what the user wants and provide satisfaction compared to ordinary searches. In the music industry, recommendations are used to provide songs that have similarities in terms of genre or theme. There are various kinds of genres in the world of music, including pop, classic, reggae and others. With genre, the difference between one song and another can be heard clearly. This genre can be analyzed by spectrogram analysis. In this study, a spectrogram analysis was developed which will be the input feature for the Convolutional Neural Network. CNN will classify and provide song recommendations according to what the user wants. In addition, testing was carried out with two different architectures from CCN, namely VGG-16 and RESNET-50. From the results of the study obtained, the best accuracy results were obtained by the VGG-16 model with 20 epochs with accuracy 60%, compared to the RESNET-50 model with more than 20 epochs. The results of the recommendations generated on the test data obtained a good similarity value for VGG-16 compared to RESNET-50.

Hyperparameter Optimization of CNN Classifier for Music Genre Classification

“…The time duration of the data affects the accuracy of results obtained, as seen in the comparison of several studies in the duration data comparison (Table 6). Research Time duration Acc % Ahmad [7] 3 seconds 95 Lau [19] 3 seconds 81.73 Zhang [10] 3 seconds 87,4 Vita [9] 30 seconds 58 Purnama [13] 30 seconds 60 Ndou [3] 30…”

“…Further, the research employed image data resulting from the spectrogram on the GTZAN dataset by comparing two CNN architectures. The results showed that VGG-16 architecture at 20 epochs performed better than the Resnet-50 architecture, with an accuracy of 60% [13].…”