This project aims to classify Blue whale calls into A-calls or not based on a training dataset provided as a part of ISI DataFest Integration 2023. A-Calls are the most commonly heard vocalization of Blue whales and play an important role in their communication and behavior.
The project uses a Convolutional Neural Network (CNN) to classify the Blue whale calls. The CNN model is trained on a training dataset consisting of ~2600 samples and validated on a ~10% samples. The trained model was then tested on a separate testing dataset consisting of ~1000 samples.
Clone the repo and cd into it. To install the required packages, run the following command:
pip install -r requirements.txt
Download the dataset from the competetion link here or using the kaggle API, run:
kaggle competitions download -c datafestintegration2023
Make sure the dataset is in the same directory as the working directory.
First preprocess the data. Install ffmpeg which is a dependancy for make_aiff.py and then preprocess the data. Run the following commands:
sudo apt install ffmpeg
python make_aiff.py
python preprocess.py
To train the model, run the following command:
python train.py
The dataset consists of labeled audio files of Blue whale calls in WAV format. The files are categorized as A-call or not based on their spectrogram.
The CNN model consists of 3 convolutional layers, followed by 2 fully connected layers, and an output layer. The model is trained with binary cross-entropy loss and Adam optimizer.
The model achieved an accuracy of 97% on the test set.
This project is licensed under the MIT license.
