Songs Classification Problem Research by Genre Based on Neural Network

Abstract

This article explores the use of neural networks for classifying musical compositions by genre based on their audio characteristics. With the increasing availability of large musical datasets and advancements in computational technologies, this topic has gained significant relevance. The article emphasizes the importance of selecting and processing audio features, particularly focusing on Mel-Frequency Cepstral Coefficients (MFCC), which reflect the key sound characteristics essential for genre identification. The approach to creating a neural network capable of effectively processing these characteristics and classifying songs is examined within the research. It outlines the main challenges associated with classification accuracy, error calculation, and the need to adapt to rapidly changing musical trends. This article also reviews and constructs a test neural network for classifying songs into three genres, creates diagrams to depict classification accuracy, and shows the distribution of each class by frequency characteristics. Additionally, it analyzes recent research in this field and highlights the importance of using MFCC. The article illuminates not only the technical aspects of creating and training neural networks for song classification but also the significance of such systems in the context of the widespread availability of musical content. It underscores the role of these systems in enhancing the quality of recommendation services, providing a more comprehensive musical experience to users, and supporting artists and musical styles. In conclusion, the article contributes valuable insights into the potential of neural networks in song genre classification, pointing to future research and development directions in this area.