https://github.com/linto-ai/sfeatpy

Library to extract MFCC features from audio signal
https://github.com/linto-ai/sfeatpy

feature-extraction mfcc mfcc-features python3 speech-processing

Last synced: 6 days ago
JSON representation

Library to extract MFCC features from audio signal

• Host: GitHub
• URL: https://github.com/linto-ai/sfeatpy
• Owner: linto-ai
• License: agpl-3.0
• Created: 2021-02-16T13:28:50.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2021-02-16T14:36:23.000Z (almost 4 years ago)
• Last Synced: 2024-12-07T20:46:02.513Z (26 days ago)
• Topics: feature-extraction, mfcc, mfcc-features, python3, speech-processing
• Language: Python
• Homepage:
• Size: 18.6 KB
• Stars: 2
• Watchers: 5
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md

Awesome Lists containing this project

README

        
# SFeatPy

![version](https://img.shields.io/github/manifest-json/v/linto-ai/sfeatpy)   [![pypi version](https://img.shields.io/pypi/v/sfeatpy)](https://pypi.org/project/sfeatpy/)

## Introduction

Python library to extract MFCC parameters.

## Installation

### pypi

```bash

pip install sfeatpy

```

### From source

```bash

git clone https://github.com/linto-ai/sfeatpy.git

cd sfeatpy

./setup.py install

```

## Usage

```python

import sfeatpy

import numpy as np

rd_signal = np.random.random(16000)

res = sfeatpy.mfcc(rd_signal,           # audio signal

                   sample_rate,         # sample_rate -- Audio sampling rate (default 16000)  

                   window_length,       # window_length -- window size in sample (default 1024)  

                   window_stride,       # window_stride -- window stride in sample (default 512)  

                   fft_size,            # fft_size -- fft number of points (default 1024) 

                   min_freq,            # min_freq -- minimum frequency in hertz (default 20) 

                   max_freq,            # max_freq -- maximum frequency in hertz (default 7000) 

                   num_filter,          # num_filter -- number of MEL bins (default 40) 

                   num_coef,            # num_coef -- number of output coeficients (default 20) 

                   windowFun,           # windowFun -- window function: 0- None | 1- hamming (default 0) 

                   preEmp,              # preEmp -- preEmphasis factor ignored on None (default 0.97) 

                   keep_first_value     # keep_first_value -- if False discard first MFCC value (default False)

                   )

res.shape

> (30,20)

```

## Limitations

* Values are not checked to keep the processing efficient.

* Works only on Mono-channel signal

## Licence

This project is under aGPLv3 licence, feel free to use and modify the code under those terms.

See LICENCE

## Used libraries

* [Numpy](http://www.numpy.org/)

* [Scipy](https://github.com/tensorflow/tensorflow)