Copyright (c) 2018 Queensland University of Technology, written by Ivan Himawan firstname.lastname@example.org
This repository contains 3D-CNN+RNN implementation for the second edition of bird audio detection challenge 2.
Detail of the environment is in req.txt (generated using 'pip freeze')
This version uses Matlab for feature extraction. The csv files are slightly modified (i.e., removing the header) to simplify the training/testing process.
Tensorflow for training 3D-CNN+RNN models.
Train with different initialization.
python cnn_3d_rnn.py 777 results would be in eval_final.csv
python cnn_3d_rnn.py 888
python cnn_3d_rnn.py 999
Average all results.
Setting the Python environment
- Download and installing conda. download url: https://repo.continuum.io/miniconda/
- Create virtual environment for python using conda.
./miniconda2/bin/conda create -n bad2 source activate bad2 pip install tensorflow-gpu==1.4.1
Feature extraction process
This version of feature extraction process use Matlab scripts to compute spectrogram.
Information regarding model training
Tensorflow is used to implement the deep architectures. Require tensorflow (>=1.4.0) with GPU support. ``bash python cnn_3d_rnn.py 777
Model ensemble can be performed by running several instances of model training (i.e., using different seed), and average the predictions.