CetaceXplain

CetaceXplain was a previous project among some of the Computer scientists in our project. It is a Python module used to understand how machine learning models classify cetacean whistle contours. Machine learning models are computer based algorithms used to recognise patterns. In this case, the patterns were dolphin whistles contours (Figure 1). Recognising patterns in these contours allows machine learning models to classify these whistles.

CetaceXplain uses the SHAP Python library. SHAP uses Shapley values, derived from economic game theory that is now being widely applied to machine learning and artificial intelligence (AI). SHAP, in this module, indicates pixels on spectrograms that are considered important to a species classifier’s decision making process.

Spectrograms highlighting the pixels that are important for a machine learning (ML) model. Each of the grey columns represent the model’s classification of the image for different dolphin species. The red pixels indicate a positive influence on the machine learning model and the blue pixels indicate a negative influence. The left image uses the model trained on coloured images, whereas the right image uses greyscale images. Removing colour may improve the model by learning that colour choice for displaying spectrograms is irrelevant.