To reduce the need for skilled clinicians in heart sound interpretation, recent studies on automating cardiac auscultation have explored deep learning approaches. However, despite the demands for large data for deep learning, the size of the heart sound datasets is limited, and no pre-trained model is available. On the contrary, many pre-trained models for general audio tasks are available as general-purpose audio representations. This study explores the potential of general-purpose audio representations pre-trained on large-scale datasets for transfer learning in heart murmur detection. Experiments on the CirCor DigiScope heart sound dataset show that the recent self-supervised learning Masked Modeling Duo (M2D) outperforms previous methods with the results of a weighted accuracy of 0.832 and an unweighted average recall of 0.713. Experiments further confirm improved performance by ensembling M2D with other models. These results demonstrate the effectiveness of general-purpose audio representation in processing heart sounds and open the way for further applications. Our code is available online which runs on a 24 GB consumer GPU at https://github.com/nttcslab/m2d/tree/master/app/circor

翻译：为减少心脏听诊解读对专业临床医生的依赖，近期关于自动化心脏听诊的研究已探索了深度学习方法。然而，尽管深度学习需要大量数据，但心脏声音数据集的规模有限，且不存在预训练模型。与之相反，许多用于通用音频任务的预训练模型可作为通用音频表示加以利用。本研究探讨了在大规模数据集上预训练的通用音频表示在心脏杂音检测迁移学习中的潜力。在CirCor DigiScope心脏声音数据集上的实验表明，近期基于自监督学习的掩码建模对偶方法（M2D）以加权准确率0.832和未加权平均召回率0.713的结果优于先前方法。实验进一步证实，将M2D与其他模型集成可提升性能。这些结果证明了通用音频表示在处理心脏声音方面的有效性，并为后续应用开辟了道路。我们的代码已在线公开，可在24 GB消费级GPU上运行，地址为https://github.com/nttcslab/m2d/tree/master/app/circor。