🎵 BioFoundation: Foundation Models for Bioacoustics

A Comparative Review of Foundation Models for Bioacoustics 🤗

A comprehensive evaluation framework for foundation models in bioacoustic analysis

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🔍 Overview

This repository contains the official implementation and evaluation framework for our paper "Foundation Models for Bioacoustics: A Comparative Review". We present a systematic comparison of state-of-the-art foundation models across multiple bioacoustic benchmarks, providing insights into their effectiveness for animal sound classification and analysis.

🎯 Key Features

• Comprehensive Evaluation: Systematic comparison of 12+ foundation models
• Multiple Benchmarks: Evaluation on BEANS and BirdSet datasets
• Flexible Framework: Easy-to-use scripts for reproducing experiments
• Standardized Protocols: Linear probing, attentive probing, and fine-tuning evaluations
• Rich Documentation: Detailed configuration and setup instructions

📊 Supported Models

Our framework evaluates the following foundation models:

Baseline General Audio Models:

• AudioMAE
• BEATs
• EAT

Bioacoustic Foundation Models:

• AVES
• BEATs NLM
• BioLingual
• Bird AVES
• BirdMAE
• ConvNeXt_BS
• Perch
• ProtoCLR
• SurfPerch
• ViT INS

🗂️ Datasets

• BEANS: Benchmark of Animal Sounds

  • Watkins Marine Mammal Dataset (31 classes)
  • Bat Calls (10 classes)
  • CBI Bird Dataset (264 classes)
  • Dog Barks (10 classes)
  • HumBugDB Mosquito Dataset (14 classes)

• BirdSet: Comprehensive bird sound benchmark

  • 8 datasets: PER, POW, NES, UHH, HSN, NBP, SSW, SNE

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🚀 Quick Start

Installation

Using Devcontainer (Recommended)

We provide a preconfigured development container for easy setup:

git submodule update --init --recursive

Manual Installation

Install dependencies using Poetry:

poetry install
poetry shell

🧪 Running Experiments

BirdSet Experiments

Use our convenient run_birdset.sh script to evaluate models on BirdSet datasets:

# Run all models on all BirdSet datasets
./projects/biofoundation/scripts/run_birdset.sh

# Run specific models
./projects/biofoundation/scripts/run_birdset.sh --models perch,aves,audiomae

# Run on specific datasets
./projects/biofoundation/scripts/run_birdset.sh --datasets PER,POW,NES

# Custom configuration
./projects/biofoundation/scripts/run_birdset.sh --models perch --datasets PER --seeds 1,2,3 --gpu 0

BEANS Experiments

Use our run_beans.sh script for BEANS benchmark evaluation:

# Run all models on all BEANS datasets
./projects/biofoundation/scripts/run_beans.sh

# Run specific models
./projects/biofoundation/scripts/run_beans.sh --models perch,aves

# Run on specific datasets
./projects/biofoundation/scripts/run_beans.sh --datasets beans_watkins,beans_cbi

# Custom configuration
./projects/biofoundation/scripts/run_beans.sh --models perch --datasets beans_watkins --seeds 1,2,3 --gpu 0

Manual Experiment Execution

For more granular control, you can run individual experiments:

# BirdSet linear probing
./projects/biofoundation/train.sh experiment=birdset/linearprobing/{model_name}

# BEANS linear probing  
./projects/biofoundation/train.sh experiment=beans/linearprobing/{model_name}

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📊 Results and Analysis

Generating Results Tables

We provide automated table generation for our comprehensive results analysis:

# Download results data from WandB report
# https://wandb.ai/deepbirddetect/BioFoundation/reports/Latex-Table-Data--VmlldzoxMjEyODQ0Ng

# Generate LaTeX tables
python projects/biofoundation/results/latex/new_table.py

The script requires beans.csv and birdset.csv files in the same directory, which can be downloaded from our WandB Report.

Hyperparameter Optimization with WandB Sweeps

We use Weights & Biases Sweeps for systematic hyperparameter optimization:

# Start a sweep
wandb sweep sweeps/base_grid.yaml

# Run sweep agents
wandb agent <sweep_id>

# Multi-GPU sweep execution
projects/biofoundation/sweeps/sweep.sh <gpu_id> <sweep_id>

Available sweep configurations:

• sweeps/base_grid.yaml: Grid search for basic parameters
• sweeps/classifier.yaml: Bayesian optimization for classifier architectures

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📝 Configuration

BEANS Dataset Configuration

To run experiments on specific BEANS datasets, modify the experiment configuration:

datamodule:
  dataset:
    dataset_name: beans_watkins # Choose dataset
    hf_path: DBD-research-group/beans_watkins # HuggingFace path
    hf_name: default
    n_classes: 31 # Number of classes

Available BEANS Datasets:

Dataset	Classes	Description
beans_watkins	31	Marine mammal vocalizations
beans_bats	10	Bat echolocation calls
beans_cbi	264	Cornell Bird Identification
beans_dogs	10	Dog bark classifications
beans_humbugdb	14	Mosquito wing-beat sounds