Light-SERNet

This is the Tensorflow 2.x implementation of our paper "Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognition", submitted in ICASSP 2022.

Run

1. Clone Repository

$ git clone https://github.com/AryaAftab/LIGHT-SERNET.git
$ cd LIGHT-SERNET/

2. Requirements

• Tensorflow >= 2.3.0
• Numpy >= 1.19.2
• Tqdm >= 4.50.2
• Matplotlib> = 3.3.1
• Scikit-learn >= 0.23.2

$ pip install -r requirements.txt

3. Data:

• Download EMO-DB and IEMOCAP(requires permission to access) datasets
• extract them in data folder

4. Prepare datasets :

Use the following code to convert each dataset to the desired size(second):

$ python utils/segment/segment_dataset.py -dp data/{dataset_folder} -ip utils/DATASET_INFO.json -d {datasetname_in_jsonfile} -l {desired_size(seconds)}

For example, for EMO-DB Dataset :

$ python utils/segment/segment_dataset.py -dp data/EMO-DB -ip utils/DATASET_INFO.json -d EMO-DB -l 3

5. Set hyperparameters and training config :

You only need to change the constants in the hyperparameters.py to set the hyperparameters and the training config.

6. Strat training:

Use the following code to train the model on the desired dataset with the desired cost function.

• Note 1: The database name is the name of the database folder after segmentation.
• Note 2: The results for the confusion matrix are saved in the result folder.

$ python train.py -dn {dataset_name_after_segmentation} -ln {cost_function_name}

For example, for EMO-DB Dataset :

$ python train.py -dn EMO-DB_3s_Segmented -ln focal

Citation

If you find our code useful for your research, please consider citing:

@article{aftab2021light,
  title={Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognition},
  author={Aftab, Arya and Morsali, Alireza and Ghaemmaghami, Shahrokh and Champagne, Benoit},
  journal={arXiv preprint arXiv:2110.03435},
  year={2021}
}