Contrastive Embeddind Learning Method for Respiratory Sound Classification

Respiratory sound classification refers to identifying adventitious sounds from given recordings automatically. Due to the difficulty of collection and the expensive manual annotation, there are only limited samples available, which impacts on learning better models. Meanwhile, a majority of these models do not explicitly encourage intra-class compactness and inter-class separability between the learned embeddings, leading to the difficulty of identifying several samples and a reduced generalization performance. To address the problems, we propose a contrastive embedding learning method, where the input is a contrastive tuple. And the composite input strategy provides more possible network inputs. By the comparison among the samples in the tuple, we can learn the slight differences among the similar samples, and the easily-confused samples are more likely to be identified. In the embedding space, we explicitly promote the intra-class compactness and inter-class separability, thereby the generalization performance is improved. Our method is evaluated on ICBHI 2017, and the classification score is increased from 75.61% of a conventional cross-entropy network to 78.18%, outperforming the state-of-the-art methods.