In this repository, you will find various techniques and methods for augmenting and processing audio data to enhance the performance of your deep learning models. Whether you're working on speech recognition, audio classification, or any other audio-related task, these techniques can help improve the robustness and generalization of your models.
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Data Augmentation Techniques: Explore different audio data augmentation techniques such as adding noise, time stretching, pitch shifting, and more.
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Data Processing Methods: Learn about preprocessing steps such as feature extraction, normalization, and spectrogram generation to prepare your audio data for deep learning models.
- Explore the provided Jupyter notebooks and Python scripts to understand each technique and method.
2.Implement the techniques in your own deep learning projects and adapt them as needed.
Contributions to this repository are welcome! If you have additional audio data augmentation techniques or data processing methods that you would like to share, feel free to open a pull request.
For any questions or suggestions, feel free to contact the repository owner at erebahi@usthb.dz.