Automatic respiratory sound classification using machine learning is a challenging task, due to large biological variability, imbalanced datasets, as well as a diversity in recording techniques used to capture the respiration signal. While datasets with annotated respiration cycles have been proposed, methods based on supervised learning using annotations only may be limited in their generalization capability. In this study, we address this issue using supervised contrastive learning, relying both on respiration cycle annotations and a spectrogram frequency and temporal masking method SpecAugment to generate augmented samples for representation learning with a contrastive loss. We demonstrate that such an approach can outperform supervised learning using experiments on a convolutional neural network trained from scratch, achieving the new state of the art. Our work shows the potential of supervised contrastive learning in imbalanced and noisy settings. Our code is released at https://github.com/ilyassmoummad/scl_icbhi2017
翻译:使用机器学习的自动呼吸声学分类是一项具有挑战性的任务,因为生物变异性很大,数据集不平衡,而且用于捕捉呼吸信号的录音技术也多种多样。虽然提出了带有附加说明呼吸周期的数据集,但仅以有监督的学习为基础的方法在一般化能力方面可能受到限制。在本研究中,我们利用有监督的对比性学习来解决这一问题,既依靠呼吸周期说明,又依靠光谱频率和时间遮蔽方法分光放大,以生成更多的样本,用于以对比性损失的方式进行演示学习。我们证明,这种方法能够利用从零到零培训的脉冲神经网络实验,超越监督性学习,从而实现艺术的新状态。我们的工作表明,在不平衡和吵闹的环境中有监督的对比性学习潜力。我们的代码在https://github.com/ilyassmoummad/scl_icbhi2017上发布。