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BirdCLEF-2023-Identify-bird-calls-in-soundscapes 4th place solution

My writeup for this solution can be found on kaggle.

Hardware

  • Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz, CPU Core=12, CPU Memory=64GB, GPU= 1 x RTX 3090

OS/platform

  • Linux Ubuntu 20.04 LTS
  • python==3.7.13

Training

Data preparation

  • Download BirdCLEF data for 2021, 2022, and 2023
  • Download additional datasets here
  • Copy the no-call directory of ff1010bird_nocall to the BirdCLEF 2023 train_audio directory.

Directory structure example

/input/
    ┣ aicrowd2020_noise_30sec/
    ┣ birdclef-2021/
        └ train_short_audio
    ┣ birdclef-2022/
        └ train_audio
    ┣ birdclef-2023/
        ├ train_audio <- add no-call
        └ train_meta_pseudo.pickle
    ┣ esc50/
    ┣ ff1010bird_nocall/
        └  ff1010bird_metadata_v1_pseudo.pickle
    ┣ train_soundscapes/
    ┣ xeno-canto/
    ┣ xeno-canto_nd/
    ┣ zenodo_nocall_30sec/
    ┣ pretrain_metadata_10fold_pseudo.pickle
    ┣ xeno-canto_audio_meta_pseudo.pickle
    ┗ xeno-canto_nd_audio_meta_pseudo.pickle
/src/
    ┗ ...

If you train on your own data

  • Get predicted values from Kaggle Models like this notebook.
  • Store the vector of predicted values as one column (teacher_preds) in the training data. like a ○○○.pickle.

Run

# -C flag is used to specify a config file
# replace NAME_OF_CONFIG with an appropiate config file name such as exp105

python pretrain_net.py -C NAME_OF_CONFIG  # for pretraining using BirdCLEF 2021, 2022

python train_net.py -C NAME_OF_CONFIG  # for training using BirdCLEF 2021, 2022

Weights of the trained model

Inference

Inference is published in a kaggle kernel here.

Ablation study

Name Public LB Private LB
BaseModel 0.80603 0.70782
BaseModel + Knowledge Distillation 0.82073 0.72752
BaseModel + Knowledge Distillation + Adding xeno-canto 0.82905 0.74038
BaseModel + Knowledge Distillation + Adding xeno-canto + Pretraining 0.8312 0.74424
BaseModel + Knowledge Distillation + Adding xeno-canto + Pretraining + Ensemble (4 models) 0.84019 0.75688

References