bird audio detection challenge 2
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functions
BirdVox_label.csv
README.md
bad_3d_2sec.py
chern.list.csv
cnn_3d_rnn.py
extracting_features.sh
featextract.m
ff1010bird_metadata.csv
poland.list.csv
prepare_train.py
req.txt
wabrlrb10k.list.csv
warblrb10k_public_metadata.csv

README.md

Copyright (c) 2018 Queensland University of Technology, written by Ivan Himawan i.himawan@qut.edu.au

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')

Quick readme:

This version uses Matlab for feature extraction. The csv files are slightly modified (i.e., removing the header) to simplify the training/testing process.

./extracting_features.sh

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.

Readme:

Setting the Python environment

  1. Download and installing conda. download url: https://repo.continuum.io/miniconda/
bash Miniconda2-latest-Linux-x86_64.sh
  1. 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.

./extracting_features.sh

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.