Metadata-Version: 2.1
Name: audiofeatures
Version: 0.1.5
Summary: Extract MFCC, spectral, and pitch features from audio files
Description-Content-Type: text/markdown
Requires-Dist: librosa
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: soundfile

# AudioFeatures

**AudioFeatures** is a lightweight Python library for extracting features from `.wav` audio files using **librosa**.
It can extract **MFCCs, spectral features, and pitch** for machine learning or audio analysis tasks.

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## Features

- Extract **MFCC mean and standard deviation** (13 coefficients)  
- Compute **Zero Crossing Rate (ZCR)**  
- Compute **RMS energy**  
- Compute **Spectral features**: centroid, bandwidth, rolloff, contrast  
- Compute **pitch mean and standard deviation**  
- Returns results as a **pandas DataFrame**

---

## Installation

```
pip install Audiofeatures
```

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## Usage

### Extract audio features from a single file

```
from Audiofeatures import extract_audio_features

df = extract_audio_features("sample.wav")
print(df.head())
```

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## Example Output

The `extract_audio_features()` function returns a **pandas DataFrame** with columns like:

```
mfcc_mean_1, mfcc_mean_2, ..., mfcc_std_13,
zcr, rms, spec_centroid, spec_bandwidth, spec_rolloff, spec_contrast,
pitch_mean, pitch_std
```

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## Dependencies

- `librosa`  
- `numpy`  
- `pandas`  

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## License

MIT License

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## Author

Dhyan Sudheer

