Abstract
Because the classification saves time in the learning process and enables this process to take place more easily, its contribution to music learning cannot be denied. One of the most valid and effective methods in music classification is music genre classification. Given the rapid progress of music production in the world and the significant increase in the number of data, the process of classifying music genres has now become too complex to be done by humans. Considering the successful results of deep neural networks in this field, the aim is to develop a deep learning algorithm that can classify 10 different music genres. To reveal the efficiency of the model by comparing it with others, we make the classification using the GTZAN dataset, which was previously used in many studies and retains its validity. In this article, we use a convolutional neural network (CNN) to classify music genres, taking into account the previous successful results. Unlike previous studies in which CNN was used as a classifier, we represent music segments in the dataset by mel frequency cepstral coefficients (MFCC) instead of using visual features or representations. We obtain MFCCs by preprocessing the music pieces in the dataset, then train a CNN model with the acquired MFCCs and determine the success of the model with the testing data. As a result of this study, we develop a model that is successful in classifying music genres by using smaller data than previous studies.
Highlights
Since it came into existence, humanity has needed to put complex events or facts into order so as to facilitate perception and understanding processes
Unlike previous studies in which convolutional neural network (CNN) was used as a classifier, we represent music segments in the dataset by mel frequency cepstral coefficients (MFCC) instead of using visual features or representations
We expound the concepts of deep learning and convolutional neural networks (CNN)
Summary
Since it came into existence, humanity has needed to put complex events or facts into order so as to facilitate perception and understanding processes. The importance of the concept of order, which provides convenience at every stage of life, in the process of acquiring knowledge cannot be denied (Soykan, 2012) From this point of view, the necessity of creating order through classification can be determined in the actions taken to provide understanding and perception. As explained by Stowell and Dixon (2011), one of the most valid methods in maintaining this order is music genre classification This process, has become almost impossible to be done by humans, owing to the increasing and diversifying music genres and songs. In this study, we aimed to develop a deep learning model that can automatically classify 10 different music genres Thanks to this model, the music classification process can be done in a shorter time and with a higher success rate than when done by humans
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
