Classify bird species based on their songs using SIamese Networks and 1D dilated convolutions.

Aditya Dutt

Last update: Dec 27, 2022

Related tags

Deep Learning audio deep-neural-networks birds batch-normalization classification spectrogram wavenet one-shot-learning librosa triplet-loss siamese-network dilated-convolution contrastive-learning

Overview

Bird-Song-Classification

The goal is to classify different birds species based on their songs/calls. Spectrograms have been extracted from the audio samples and used as features for classification. A Siamese network based on 1D dilated convolutions is used here. Model is trained using triplet loss. I am using the British Birdsong Dataset available on Kaggle for this experiment.

Download the data from here. This dataset is a subset of Xeno-Canto database. Siamese Networks along with dilated 1D convolutions are used here to classify 9 different bird species.

Confusion Matrix of testset:

Scatter plot of embeddings after applying PCA:

Note: If you are having this error: AttributeError: module 'keras.utils.generic_utils' has no attribute 'populate_dict_with_module_objects'.
Type: pip uninstall tf-nightly
pip uninstall tf-estimate-nightly
pip install tensorflow --upgrade --force-reinstall

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Owner

Aditya Dutt

ML PhD Researcher

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Classify bird species based on their songs using SIamese Networks and 1D dilated convolutions.

Related tags

Overview

Bird-Song-Classification

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