| | --- |
| | tags: |
| | - fastai |
| | --- |
| | |
| | This model was trained to as part of collaboration between [Mote Marine Laboratory & Aquarium](https://mote.org), [Southeast Coastal Ocean Observing Regional Association](https://secoora.org), and [Axiom Data Science](https://axiomdatascience.com) to develop a model capable of detecting and classifying fish vocalizations from audio files collected from hydrophones. |
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| | More information available at [the project archive repo](https://github.com/axiom-data-science/project-classify-fish-sounds). |
| |
|
| | --- |
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|
| | # Model card |
| |
|
| | ## Model description |
| |
|
| | This model was trained on spectrograms |
| |
|
| | A [reproducible Jupyter notebook](https://github.com/axiom-data-science/project-classify-fish-sounds/blob/main/notebooks/train-resnet101-fastai.ipynb) describing the training of the model is available in the archive repo. |
| |
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| | ## Intended uses & limitations |
| |
|
| | The model was intended to be a proof on concept to aid researchers identify fish vocalizations through vast amounts of audio data collected from hydrophones. |
| | Although the training data was collected using multiple devices in multiple locations, the model may not be generally applicable to other uses. |
| |
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| | ## Training and evaluation data |
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| | A training set of spectrograms of fish calls was created based on annotations of fish sounds in passive acoustic recordings by a hydrophone were provided by Jim Locascio, Max Fullmer, and volunteers from the [Mote Marine Laboratory & Aquarium](https://mote.org). |
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| | Due to severe imbalances in the number of samples per class, the training involved both under-sampling classes with many samples and over-sampling classes with few classes so that the model was trained on 50 samples per class. This number was derived in a completely ad-hoc fashion based on the distribution of class samples. |
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|
| | ### Class label description |
| |
|
| | | Call Index | Description | |
| | |------------|-------------| |
| | | 0 | Background noise (no fish vocalizations) | |
| | | 1 | Black grouper 1 | |
| | | 2 | Black grouper 2 | |
| | | 3 | Black grouper grunt | |
| | | 4 | Black grouper spawning rush | |
| | | 5 | Black grouper chorus < 50% of file | |
| | | 6 | Black grouper chrous > 50% of file | |
| | | 8 | Unidentified sound type | |
| | | 9 | Red grouper 1 | |
| | | 10 | Red grouper 2 | |
| | | 17 | Red hind 1 | |
| | | 18 | Red hind 2 | |
| | | 19 | Red hind 3 | |
| | | 25 | Goliath grouper 1 | |
| | | 27 | Multi-phase goliath grouper | |
| | | 28 | Sea trout chorus | |
| | | 29 | Silver perch call | |
| |
|
| | ### Class indices in trained model |
| |
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| | Some classes did not meet the training criteria, high signal-to-noise ratio and minimum call overlap, and were therefore excluded from the model training. |
| | As such, the number of classes represented in the trained model is few than the amount of labeled classes in the training set. |
| |
|
| | | Call Index | Description | |
| | -------------|-------------| |
| | |0 | No call | |
| | |1 | Black grouper call | |
| | |2 | Black grouper call 2 | |
| | |3 | Black grouper grunt | |
| | |4 | Unidentified sound | |
| | |5 | Red grouper 1 | |
| | |6 | Red grouper 2 | |
| | |7 | Red hind 1 | |
| | |8 | Red hind 2 | |
| | |9 | Red hind 3 | |
| | |10 | Goliath grouper | |
| | |11 | Goliath grouper multi-phase | |
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|
