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extras/ (0, 2023-10-13)
extras/test-gb.wav (2993162, 2023-10-13)
extras/tone-gb.wav (748316, 2023-10-13)
main.py (152, 2023-10-13)
main_AFE(FEFA).ipynb (5982922, 2023-10-13)

# Audio Processing Feature Extraction ## Overview This project provides code snippets for audio processing and feature extraction using the Librosa library in Python. Librosa is a powerful tool for music and audio analysis, offering functionalities for loading audio files, visualizing waveforms, creating spectrograms, and extracting features like spectral centroid and MFCCs. ## Credits - Code snippets and inspiration from: - [Towards Data Science: Extract Features of Music](https://towardsdatascience.com/extract-features-of-music-75a3f9bc265d) - [Kaggle Notebook: Feature Extraction from Audio](https://www.kaggle.com/code/ashishpatel26/feature-extraction-from-audio/notebook) - [extras/test-gb.mav (excerpt from the track "Gentle Blade" from the Sekiro: Shadows Die Twice Original Soundtrack - Yuka Kitamura)](https://www.youtube.com/watch?v=tOzQ_OqnY2s) - [ChatGPT](https://chat.openai.com) ## How it Works The code snippets cover the following aspects: 1. **Loading and Playing Audio** - Load an audio file. - Play the audio within a Jupyter Notebook. 2. **Waveform Visualization** - Visualize the audio waveform, showcasing its amplitude and structure. 3. **Spectrogram Visualization** - Generate and display a spectrogram. - Visualize the log-scaled spectrogram for detailed frequency content. 4. **Creating a Sine Wave** - Generate a synthetic sine wave signal for testing purposes. 5. **Feature Extraction** - Display the waveform of an audio signal. - Explore feature extraction techniques. 6. **Spectral Centroid Visualization** - Compute and visualize the spectral centroid, representing the center of mass of frequencies in the sound. 7. **MFCC (Mel-Frequency Cepstral Coefficients) Visualization** - Compute and display MFCCs, a representation of the short-term power spectrum of a sound. ## Packages Used - `librosa`: Core library for audio processing. - `numpy`: Used for numerical operations. - `matplotlib`: Required for generating visualizations. - `sklearn`: Used for normalization in some visualizations. ## Setup Make sure to install the required packages using the following: ```bash pip install librosa numpy matplotlib scikit-learn

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