Untitled

Creating a feature extractor for jazz music involves analyzing the audio to extract meaningful characteristics that can be used for various purposes, such as classification, recommendation, or even generative music. Below is a Python example demonstrating how to extract audio features from jazz music using common libraries such as `librosa`, `numpy`, and `pandas`. 

### Prerequisites

You'll need to install the following libraries if you haven’t already:

```bash
pip install librosa numpy pandas
```

### Example Code

Here’s a simple script that extracts several basic audio features from a jazz music file. Features include tempo, spectral centroid, chroma, MFCCs (Mel-frequency cepstral coefficients), and more.

```python
import librosa
import numpy as np
import pandas as pd

def extract_features(file_path):
    # Load the audio file
    y, sr = librosa.load(file_path, sr=None)
    
    # Extracting various features
    tempo, _ = librosa.beat.beat_track(y=y, sr=sr)
    spectral_centroid = np.mean(librosa.feature.spectral_centroid(y=y, sr=sr))
    spectral_bandwidth = np.mean(librosa.feature.spectral_bandwidth(y=y, sr=sr))
    rolloff = np.mean(librosa.feature.spectral_rolloff(y=y, roll_percent=0.85, sr=sr))
    zero_crossing_rate = np.mean(librosa.feature.zero_crossing_rate(y=y))

    # Chroma features
    chroma_stft = np.mean(librosa.feature.chroma_stft(y=y, sr=sr))
    chroma_cens = np.mean(librosa.feature.chroma_cens(y=y, sr=sr))
    
    # MFCCs
    mfccs = np.mean(librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13), axis=1)

    # Compile the features into a dictionary
    features = {
        'tempo': tempo,
        'spectral_centroid': spectral_centroid,
        'spectral_bandwidth': spectral_bandwidth,
        'rolloff': rolloff,
        'zero_crossing_rate': zero_crossing_rate,
        'chroma_stft': chroma_stft,
        'chroma_cens': chroma_cens,
    }
    
    # Add MFCCs to the features dictionary
    for i in range(len(mfccs)):
        features[f'mfcc_{i+1}'] = mfccs[i]

    return features

# Example usage
if __name__ == "__main__":
    file_path = 'path_to_your_jazz_audio_file.wav'  # replace with your jazz music file path
    features = extract_features(file_path)
    
    # Convert features to DataFrame
    features_df = pd.DataFrame([features])
    print(features_df)
```

### Feature Description

1. **Tempo**: The tempo of the piece, measured in beats per minute (BPM).

2. **Spectral Centroid**: Indicates where the center of mass of the spectrum is located, often correlating with brightness.

3. **Spectral Bandwidth**: Measures the width of the range of the spectrum; a higher value indicates more complexity.

4. **Spectral Rolloff**: The frequency below which a certain percentage of the total spectral energy is contained; useful for distinguishing tonal from non-tonal sounds.

5. **Zero Crossing Rate**: The rate at which the signal changes from positive to negative or back; can indicate noisiness.

6. **Chroma Features**:
   - **Chroma STFT**: Relates to the energy distribution across the 12 different pitch classes.
   - **Chroma CENS**: Similar to Chroma STFT but more robust to dynamics.

7. **MFCCs**: Mel-frequency cepstral coefficients are widely used features in audio processing, capturing the timbral aspects of the sound. We extract 13 coefficients in this example.

### Notes

- **File Path**: Be sure to replace `'path_to_your_jazz_audio_file.wav'` with the actual path to a jazz music file in WAV format.
- **Audio Format**: This example assumes the input audio file is in a supported format (WAV, MP3, etc.). `librosa` can handle various formats.
- **Feature Scaling**: Depending on your application, you might want to normalize or scale these features before feeding them into a machine learning model.

This is a basic framework for feature extraction and can be expanded upon by adding more complex features or using additional libraries like `madmom` or `scikit-learn` for more advanced audio analysis and machine learning tasks.