Entry for Bird Audio Detection Challenge

For more information about the challenge, see the project website or the paper that summarizes the challenge.

Important Links

• Bird Audio Detection Challenge

Other Entries

• RSPB's entry
• qiuqiangkong's entry
• topel's entry

Getting Started

This should be enough to get you training and evaluating code running:

Clone the repository:

1. git clone git@github.com:UkyVision/bird-audio-detection.git
2. Create a branch for your work: git checkout -b feature/{SHORT NAME OF FEATURE}

Install tensorflow:

1. Install Miniconda.
2. Create a virtual environment called tensorflow: conda create -n tensorflow python=2.7 ipython.
3. Logout and log back in
4. Start the virtual environment: source activate tensorflow
5. Install Tensorflow using the Anaconda provided instructions. Short version: find the correct setting for TF_BINARY_URL from the tensorflow website, then run pip install --ignore-installed --upgrade $TF_BINARY_URL.

Prepare the datasets:

1. You have to have scikit-learn library installed. If not, install by typing conda install scikit-learn and follow instructions
2. cd ./src/dataset
3. python download_and_extract.py : note this might take a while
4. python make_dataset.py : splits dataset into 10 folds
5. Datasets will be downloaded to ../../data/ while the folds will be written to the current dataset directory

Run the training script:

1. cd ./src/
2. python main.py: this will put checkpoints in the checkpoint directory
3. python evaluate.py: this uses the checkpoints to generate an output script

Training multiple models:

1. cd ./src/driver
2. ./driver.sh : this uses GNU parallel to train multiple models. it is currently configured to work on a machine with two GPUs

Use features extracted from soundNet to train traditional ML approaches:

In this approach for Bird Audio Detection, we push the audio files through soundNet and extract the features at different layers. Then train different basic ML approaches using scikit-learn package. The code to train linear_SVM in ./src/scikit

Contributing

If you are new to git and github, I encourage you to read this guide to contributing. Basically, read through the issues and/or talk to the team leaders to see what would be a useful contribution. Then, you can either:

• fork the repo (please keep it private for now), write your code, verify that it works, and submit a pull request into master when it is ready
• create a branch 'feature/*' and submit a pull request into master when you are ready

Team

• Nathan Jacobs (team lead)
• Weilian "William" Song
• Tawfiq Salem