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Music Genre Classification Using Deep Learning Techniques

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Introduction

Music genre classification is a crucial task in the field of Music Information Retrieval (MIR), where the aim is to categorize music tracks into genres such as Pop, Rock, Jazz, etc. This project proposal focuses on developing a music genre classification system using deep learning techniques, leveraging recent advancements in representation learning and multimodal data integration.

Background

Traditional methods for music genre classification often rely on handcrafted features extracted from audio signals, such as Mel Frequency Cepstral Coefficients (MFCCs). However, these methods may not fully capture the complexity and diversity of musical genres. Recent research has demonstrated that deep learning approaches, particularly Convolutional Neural Networks (CNNs) and Convolutional Recurrent Neural Networks (CRNNs), can significantly improve classification performance by automatically learning feature representations from raw audio data.

Project Objective

The primary objective of this project is to develop a robust music genre classification system that utilizes deep learning architectures to enhance accuracy and efficiency. The system aims to outperform traditional methods by integrating audio and visual data representations.

Methodology

1. Data Collection and Preprocessing

  • Datasets: Utilize the GTZAN Music Genre dataset for training and evaluation. This dataset is widely used in music genre classification research and contains diverse music tracks across various genres.
  • Feature Extraction: Extract spectrograms from audio files to serve as input to the neural network models.

2. Model Architecture

  • CNN-CRNN Hybrid Model: Implement a hybrid model combining CNN layers for spatial feature extraction from spectrograms and RNN layers for capturing temporal dependencies.
  • Multimodal Approach: Integrate visual data representations with audio features to enhance classification performance.

3. Training and Evaluation

  • Training: Use categorical cross-entropy loss function for training, with backpropagation through time for RNN components.
  • Evaluation Metrics: Measure performance using accuracy, precision, recall, and F1-score.

Expected Outcomes

The proposed system is expected to achieve higher accuracy in music genre classification compared to traditional methods. By utilizing deep learning techniques and multimodal data integration, the system should effectively handle the variability in musical styles and improve genre prediction accuracy.

Conclusion

This project seeks to advance the field of music genre classification by developing a state-of-the-art system capable of accurately categorizing music tracks into genres. The integration of CNNs, CRNNs, and multimodal data is anticipated to provide significant improvements in performance.

For further details on related research, please refer to the paper "Music Genre Classification Using Deep Learning" available at https://ieeexplore.ieee.org/document/8489472.

Dataset link: GTZAN Music Genre Dataset

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