1.1 Development Environment

# Find repo on host machine
cd ~/code/batbot

# Build Docker image
docker build -t kitware/batbot:latest .

# Start Docker container using image
docker run \
   -it \
   --rm \
   --entrypoint bash \
   --name batbot \
   -v $(pwd):/code \
   kitware/batbot:latest

########################
# Inside the container #
########################

# Activate Python environment
source /venv/bin/activate

# Install local version
pip install -e .

# Run batbot
batbot --help

1.2 Spectrogram Extraction

Here are the steps for extracting the compressed spectrogram:

• Create the STFT

  • Load the original waveform at the original sample rate

  • Resample waveform to 250kHz

  • Convert to a STFT spectrogram (fft=512, method=blackmanharris, window=256, hop=16)

  • Convert complex power STFT to amplitude STFT (dB)

• Normalize the STFT

  • Trim STFT to minimum and maximum frequencies (5kHz to 120kHz)

  • Subtract the per-freqency median dB (reduce any spectral bias / shift)

  • Set global dynamic range to -80 dB from the global maximum amplitude

  • Calculate the global median non-minimum dB (greater than -80dB)

  • Calculate the median absolute deviation (MAD)

  • Autogain the dynamic range to (5 * MAD) below the global amplitude median, if necessary

• Quantize the STFT

  • Quantize the floating-point amplitude STFT to a 16-bit integer representation spanning the full dynamic range (65,536 bins)

  • Vertically flip the spectrogram (low frequencies on bottom) and convert to a C-contiguous array

• Find Candidate Chirps

  • Create a 12ms sliding window with a 3ms stride

  • Keep the time windows that show a substantial right-skew across 10% of the frequency range

  • Add any user-provided time windows (annotations) to the found candidates windows

  • Merge any overlapping time windows into a set of contiguous time ranges

  • Tighten the candidate time ranges (and separate as needed) by repeating the same skew-based filter with a smaller sliding window and stride

• Extract Chirp Metrics

  • for each candidate chirp

  • Start: First, find the peak amplitude location.

  • Step 1 - Normalize the chirp to the full 16-bit range. Calculate a histogram and identify the most common dB and standard deviation. Scale the amplitude values using an inverted PDF, weighting each value by its inverse probability of being noise (values below the most common dB are set to zero)

  • Step 2 - Apply a median filter and re-normalize

  • Step 3 - Apply a morphological open operation

  • Step 4 - Blur the chirp (k=5) and re-normalize

  • Step 5 - Find contours using the “marching squares” algorithm and select the one that contains the peak amplitude. Extract the convex hull of the contour and smooth the resulting outline

  • Step 6 - Extract a segmentation mask for the contour

  • Step 7 - Locate the harmonic (doubling the frequency) and echo (right edge of the contour to the end of the chirp time range) regions. Remove any overlapping noise from the chirp contour.

  • Step 8 - Locate the start, end, and characteristic frequency points (peak amplitude) and calculate an optimization cost grid for the contour using the masked amplitudes.

  • Step 9 - Solve a minimum distance optimization using A* that also maximizes the amplutide values from start to end points.

  • Step 10 - Smooth the contour path, extract the contour’s slope, then identify the knee, heel, and other defining attributes.

  • End: Finally, if any of the above steps fails, or the chirp’s attributes do not make semantic sense, then skip the candidate chirp.

• Create Output

  • Collect all valid chirps regions and metadata, create a compressed spectrogram

  • Write the 16-bit spectrogram as a series of 8-bit JPEGs image chunks (max width per chunk 50k pixels)

  • Write the file and chirp metadata to a JSON file.

1.3 How to Install

pip install batbot

or, from source:

git clone https://github.com/Kitware/batbot
cd batbot
pip install -e .

To then add GPU acceleration, you need to replace onnxruntime with onnxruntime-gpu:

pip uninstall -y onnxruntime
pip install onnxruntime-gpu

1.4 How to Run

You can run the Gradio demo with:

python app.py

To run with Docker:

cd batbot
docker run \
  -it \
  --entrypoint bash \
  --rm \
  --name batbot \
  -v $(pwd):/code \
  kitware/batbot:latest

or to run the Gradio app:

docker run \
  -it \
  --rm \
  -p 7860:7860 \
  --gpus all \
  --name batbot \
  kitware/batbot:latest \
  python3 app.py

To run with Docker Compose:

version: "3"

services:
  batbot:
    image: kitware/batbot:latest
    command: python3 app.py
    ports:
      - "7860:7860"
    environment:
      CLASSIFIER_BATCH_SIZE: 512
    restart: unless-stopped
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              device_ids: ["all"]
              capabilities: [gpu]

and run docker compose up -d.

1.5 How to Build and Deploy

3.1 Tests and Coverage

You can run the automated tests in the tests/ folder by running:

pip install -r requirements/optional.txt
pytest

You may also get a coverage percentage by running:

coverage html

and open the coverage/html/index.html file in your browser.

3.2 Building Documentation

There is Sphinx documentation in the docs/ folder, which can be built by running:

cd docs/
pip install -r requirements/optional.txt
sphinx-build -M html . build/

3.3 Logging

The script uses Python’s built-in logging functionality called logging. All print functions are replaced with log.info(), which sends the output to two places:

1. the terminal window, and

2. the file batbot.log

3.4 Code Formatting

It’s recommended that you use pre-commit to ensure linting procedures are run on any code you write. See pre-commit.com for more information.

Reference pre-commit’s installation instructions for software installation on your OS/platform. After you have the software installed, run pre-commit install on the command line. Now every time you commit to this project’s code base the linter procedures will automatically run over the changed files. To run pre-commit on files preemtively from the command line use:

pip install -r requirements/optional.txt
pre-commit run --all-files

The code base has been formatted by Black. Furthermore, try to conform to PEP8. You should set up your preferred editor to use flake8 as its Python linter, but pre-commit will ensure compliance before a git commit is completed. This will use the flake8 configuration within setup.cfg, which ignores several errors and stylistic considerations. See the setup.cfg file for a full and accurate listing of stylistic codes to ignore.