Skip to content

g-zucatelli/IDEA_App

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

3 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

IDEA - Acoustic Emotion IDentification App

The IDEA App is the IDentificador de Emoções Acústicas (in original Portuguese).

This is a PoC IONIC App which design to identify emotions (happiness, anger, neutral and sadness) in speech [1] [2].

The IDEA App was an integral part of my undergraduate research and undergraduate thesis during the Bachelor in Electronic Engineering at the Military Institute of Engineering (IME).

The undergraduate assistant research was conducted at the Laboratory of Acoustic Signal Processing (LASP) in the mains topics of: acoustic emotion identification, speech enhancement, speaker recognition and speech intelligibility.

Awards:

  • Recipient of the CREA-RJ Award, given annually to the best undergraduate thesis among all graduating electronic engineers in Rio de Janeiro by the Regional Counsil of Engineering and Agronomy. (2017)
  • Recipient of the Best Undergraduate Research Award, given at the annually IME Undergraduate Research Meeting to best presented research. (2016)

App Interface Snapshots

Main interfaces:

  • Emotion Identification
  • Configuration
  • Info

The PoC was developed with a simple design of START / STOP buttoms that activate/deactivate the algorithms of (1) audio capture, (2) pre-processing, (3) Mel-Frequency Cepstral Coefficient (MFCC) feature extraction [3] and (4) trained stochastic Gaussian Mixture Model (GMM) inference.

Pages

For each model prediction, i.e. identified emotion with maximum probability, the interface is than updated as illustrated below for either Anger, Happiness, Neutral and Sadness emotions, respectively.

Emotion ID

After the START activation, the IDEA App performs 4 inferences per second for a given selected language.


References

[1] Darwin, C. R. 1872. "The expression of the emotions in man and animals". London: John Murray. 1st edition.

[2] B. Wang and M. Lugger, "Emotion recognition from speech signals using new harmony features", Signal Processing, 2010.

[3] Reynolds, Douglas A., and Richard C. Rose. ”Robust text-independent speaker identification using Gaussian mixture speaker models.” IEEE transactions on speech and audio processing, 3.1, 1995.


Important Note: This repository is not maintained.

About

IDEA App - The Acoustic Emotion IDentification App

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published