Git Product home page Git Product logo

compact_ser's Introduction

Compact Graph Architecture for Speech Emotion Recognition


Feb 02, 2021

  • First release of the project.

In this project, we propose a deep graph approach to address the task of speech emotion recognition. A compact, efficient and scalable way to represent data is in the form of graphs. Following the theory of graph signal processing, we propose to model speech signal as a cycle graph or a line graph. Such graph structure enables us to construct a Graph Convolution Network (GCN)-based architecture that can perform an accurate graph convolution in contrast to the approximate convolution used in standard GCNs. We evaluated the performance of our model for speech emotion recognition on the popular IEMOCAP database.

Dependency installation

The code was successfully built and run with these versions:

pytorch-gpu 1.2.0
cudnn 7.6.4
cudatoolkit 10.0.130
opencv 3.4.2
scikit-learn 0.21.2

Note: You can also create the environment I've tested with by importing environment.yml to conda.


Preprocessing Data

The process for IEMOCAP database is in preprocess directory. The process converts the database into one txt file including graph structure and node attributes.

Note: you can download the processed data from here and put in this directory:

/dataset/
  IEMOCAP/
    IEMOCAP.txt

Training

You can train the model with running main.py .

usage: main_Inception.py

optional arguments:
  -h, --help          Show this help message and exit
  -device             Which gpu to use if any
  -batch_size         Input batch size for training
  -iters_per_epoch    Number of iterations per each epoch
  -epochs             Number of epochs to train
  -lr                 Learning rate
  --num_layers        Number of inception layers
  --hidden_dim        Number of hidden units for MLP
  --final_dropout     Dropout for classifier layer
  --Normalize         Normalizing data
  --patience          Patience for early stopping

Reference

ArXiv's paper

@article{shirian2020compact,
  title={Compact Graph Architecture for Speech Emotion Recognition},
  author={Shirian, Amir and Guha, Tanaya},
  journal={arXiv preprint arXiv:2008.02063},
  year={2020}
}




compact_ser's People

Contributors

amirsh15 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.