Assessment of Data Augmentation and Transfer Learning for Making PIG Cough Classifier Robust to Changing Farm Conditions

Lately, the monitoring of the respiratory health status using sound signals for pig cough classification has caught the attention of the research community. In this paper, existing data augmentation techniques for Sound Event Classification (SEC) and Transfer Learning (TL) schemes are evaluated in the scenario of pig cough classification. Specifically, we talk about a) Deep Learning (DL) and TL as a popular choice for SEC and b) Enhancing model robustness using data augmentation which adds (realistic) acoustic variability to the data without requiring additional annotations. The pig cough dataset recorded in commercial farm environments is used and divided into two categories (typel and type2) based on acoustical properties of the farms. Transfer learning with data augmentation strategies are used to explore the generalization of the pig cough classifier to changing conditions. Overall, data augmentation methods improved the performance when the model was trained on typel data and tested on type2 data. A maximum improvement in Fl-score of 3.69 percentage points and reduction of 1.66 percentage points in the Fl-score standard deviation (FI-SD) was achieved with a OpenL3 based model using a hybrid data augmentation. OpenL3 based models emerge as a light weight alternative to ResNet based models for this task. In conclusion, the findings of this study call for domain specific methods to achieve classifier adaptability to changing environmental acoustical conditions in farms.