Figure 2

Overview of proposed PFL-SD and existing AI audio-based diagnosis methods. In the conventional method (baseline), \(\theta ^{*}\) is correctly learned from the training data, but the network does not directly use training data for (post-training) diagnosis. In the proposed method, our network improves the performance by using training data even during diagnosis (post-training), that is, the distance between the training and inference samples is determined during classification. Hence, the proposed method improves its diagnostic performance with few training samples and correctly identifies stridor patches in an audio recording. The red box denotes the baseline method does not directly use training data for (post-training) diagnosis. The green box denotes the proposed method utilizes training data in the inference process (the training data is not evaluated additionally).