https://github.com/mohammedyunus009/dnn_acoustic_rec

A acoustic sound or environmental sound recogniser, uses deep neural networks to train on models
https://github.com/mohammedyunus009/dnn_acoustic_rec

deep-learning deep-neural-networks extract-features keras sound sound-processing sound-synthesis soundcloud-api tensorflow

Last synced: 7 days ago
JSON representation

A acoustic sound or environmental sound recogniser, uses deep neural networks to train on models

• Host: GitHub
• URL: https://github.com/mohammedyunus009/dnn_acoustic_rec
• Owner: mohammedyunus009
• License: apache-2.0
• Created: 2018-03-30T10:45:27.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-05-22T19:26:19.000Z (over 6 years ago)
• Last Synced: 2024-09-23T04:42:15.774Z (11 days ago)
• Topics: deep-learning, deep-neural-networks, extract-features, keras, sound, sound-processing, sound-synthesis, soundcloud-api, tensorflow
• Language: Python
• Homepage:
• Size: 27.3 KB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Introduction (machine learning ,sound recognition)

This is a keras implementation project

This project is used to recognize environmental sounds ,with a high accuracy of 80% percent ,it has a deep neural network (LSTM can be implemented),can be used to train on the extracted features of audio files in 

[https://github.com/mohammedyunus009/dev_datasets](https://github.com/mohammedyunus009/dev_datasets) and [https://github.com/mohammedyunus009/datasets](https://github.com/mohammedyunus009/datasets).

It has the capability to recognize on 15 diffrent classes of sounds such as :

'bus'  'cafe/restaurant'  'car'  'city_center'  'forest_path'  

'grocery_store'  'home'  'beach'  'library'  'metro_station'  

'office'  'residential_area'  'train'  'tram'  'park'

vtu under graduate project (visvervaraya university of technology)

# Requirements

This library runs with keras. 

`pip install -r requirements.txt`

OR for conda and linux users run 

`sh setup.sh`

# Quickstart

STEP 1

configure the config file in `src` folder

STEP 2 

*`python calculate_logmel.py` to extract features and pickle in memory

STEP 3

* `python kera_model.py --dev_train`  to run in development mode (train on development dataset)

* `python kera_model.py --eva_train`  to run in evaluation mode (train on evaluation dataset)

Reconfigure the configuration file in src

* `python kera_model.py --dev_recognize`  used to calculate the accuracy of the model in development mode

* `python kera_model.py --eva_recognize`  used to calculate the accuracy of the model in evaluation mode

STEP 4

* `python session.py` used to put in production and test new files

Contact me for more information