Thesis Abstract

Abstract
This thesis explores the application of deep learning techniques for music genre classification. With the exponential growth of digital music content, automated music genre classification has become essential for various music-related applications. The primary objective of this research is to develop a robust deep learning model that can effectively classify music tracks into different genres based on their audio features. The study begins with a comprehensive review of existing literature on music genre classification, deep learning, and related techniques. Various deep learning architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been widely used for audio analysis tasks and will be explored in this research. The research methodology involves collecting a diverse dataset of music tracks spanning multiple genres and extracting relevant audio features. These features will be used to train and evaluate different deep learning models for music genre classification. The performance of the models will be assessed using metrics such as accuracy, precision, recall, and F1 score. The findings of this study demonstrate the effectiveness of deep learning models in accurately classifying music tracks into different genres. The results will be discussed in detail, highlighting the strengths and limitations of the proposed approach. In conclusion, this thesis contributes to the field of music information retrieval by showcasing the potential of deep learning techniques for music genre classification. The implications of this research extend to music recommendation systems, content tagging, and music content analysis. Future research directions, including model optimization and scalability, will also be discussed. Keywords Deep learning, Music genre classification, Convolutional neural networks, Recurrent neural networks, Audio feature extraction, Music information retrieval.

Thesis Overview

The project titled "Deep Learning for Music Genre Classification" aims to explore the use of deep learning techniques in automatically classifying music genres. Music genre classification is a fundamental task in music information retrieval and has numerous real-world applications, such as content recommendation, music indexing, and personalized playlists generation. Traditional methods for music genre classification often rely on handcrafted features and shallow learning algorithms, which may not capture the complex patterns and nuances present in music data. Deep learning, a subset of machine learning that leverages artificial neural networks to learn intricate patterns directly from data, has shown promising results in various domains, including image recognition, natural language processing, and speech recognition. In the context of music genre classification, deep learning models have the potential to automatically learn hierarchical representations of audio features, allowing for more accurate and robust genre classification. The research overview will delve into the current state-of-the-art techniques in music genre classification, highlighting the limitations of existing methods and the motivation for leveraging deep learning models. The project will involve preprocessing and feature extraction of audio data, training deep neural networks on a large dataset of annotated music tracks, and evaluating the performance of the models based on various metrics such as accuracy, precision, recall, and F1 score. Furthermore, the research will explore different deep learning architectures suitable for music genre classification, including convolutional neural networks (CNNs) for extracting spatial features from spectrograms or waveforms, recurrent neural networks (RNNs) for capturing temporal dependencies in music sequences, and hybrid models that combine both CNNs and RNNs for capturing both local and global features in music data. The research overview will also discuss the experimental setup, including the dataset used for training and evaluation, the evaluation metrics employed, and the methodology for hyperparameter tuning and model selection. Additionally, the overview will highlight the significance of the project in advancing the field of music information retrieval and its potential impact on real-world applications such as music recommendation systems and music streaming platforms. In conclusion, the project "Deep Learning for Music Genre Classification" aims to push the boundaries of music genre classification using state-of-the-art deep learning techniques, with the ultimate goal of developing more accurate and efficient models for automatically categorizing music into different genres.