Respiratory sound classification (RSC) is challenging due to varied acoustic
signatures, primarily influenced by patient demographics and recording
environments. To address this issue, we introduce a text-audio multimodal model
that utilizes metadata of respiratory sounds, which provides useful
complementary information for RSC. Specifically, we fine-tune a pretrained
text-audio multimodal model using free-text descriptions derived from the sound
samples' metadata which includes the gender and age of patients, type of
recording devices, and recording location on the patient's body. Our method
achieves state-of-the-art performance on the ICBHI dataset, surpassing the
previous best result by a notable margin of 1.17%. This result validates the
effectiveness of leveraging metadata and respiratory sound samples in enhancing
RSC performance. Additionally, we investigate the model performance in the case
where metadata is partially unavailable, which may occur in real-world clinical
setting.

通过利用呼吸音样本的元数据，我们引入了一个文本 - 音频多模态模型来改进呼吸音分类的性能，并且在治疗临床环境中部分元数据不可用的情况下也进行了研究验证。

BTS：基于元数据辅助的呼吸音分类的文本和声音模态的桥接

BTS: Bridging Text and Sound Modalities for Metadata-Aided Respiratory  Sound Classification

Deep generative models have emerged as a promising approach in the medical
image domain to address data scarcity. However, their use for sequential data
like respiratory sounds is less explored. In this work, we propose a
straightforward approach to augment imbalanced respiratory sound data using an
audio diffusion model as a conditional neural vocoder. We also demonstrate a
simple yet effective adversarial fine-tuning method to align features between
the synthetic and real respiratory sound samples to improve respiratory sound
classification performance. Our experimental results on the ICBHI dataset
demonstrate that the proposed adversarial fine-tuning is effective, while only
using the conventional augmentation method shows performance degradation.
Moreover, our method outperforms the baseline by 2.24% on the ICBHI Score and
improves the accuracy of the minority classes up to 26.58%. For the
supplementary material, we provide the code at
this https URL

我们提出了一种简单而有效的深度生成模型，用于增强医学影像中的数据稀缺问题，并通过对抗性微调方法在呼吸音分类上取得了优越的性能。