Respiratory Sound Classification via Self-Supervised Contrastive Pre-Training
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Solution Overview
Problem
Current methods for diagnosing respiratory conditions like COVID-19 using respiratory sounds face challenges due to the need for labeled data, high annotation costs, and privacy concerns, limiting the effectiveness and applicability of fully-supervised approaches.
Innovation Solution
A self-supervised learning framework that uses a contrastive pre-training phase to learn robust numerical representations of respiratory sounds without labeled data, followed by a classification phase with a pre-trained feature encoder and ensemble architecture, reducing reliance on labeled data and improving classification performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fully-supervised learning methods are used for respiratory sound classification, then classification accuracy can be improved, but annotation costs and dependency on labeled data increase
Solution Approach 1:
The patent applies contrastive pre-training as a preliminary action before the main classification task. The feature encoder is first pre-trained on unlabeled respiratory sounds using contrastive learning to learn robust representations, then fine-tuned with labeled data for classification. This two-stage approach reduces the amount of labeled data needed while maintaining high classification accuracy.
2Reliability
If more labeled data is collected for training, then classification performance improves, but privacy concerns and annotation costs worsen
Solution Approach 1:
The system performs self-service learning by utilizing unlabeled respiratory sounds for contrastive pre-training. The model learns meaningful features from unlabeled data itself, reducing dependency on externally annotated labeled data. This self-supervised approach maintains high performance while minimizing privacy concerns associated with collecting and storing labeled patient data.
3Measurement precision
If traditional supervised training is used, then model convergence is achieved, but training time and computational resources increase due to data preprocessing
Solution Approach 1:
The contrastive pre-training phase serves as a preliminary action that prepares the feature encoder with robust representations before the main classification training. This pre-training on unlabeled data accelerates convergence during fine-tuning with labeled data, reducing overall training time and computational resources required.
4Quantity of substance
If unlabeled data is used for training, then annotation costs decrease, but classification accuracy may deteriorate
Solution Approach 1:
The patent uses unlabeled data for contrastive pre-training as a preliminary step to learn robust feature representations. This pre-training on unlabeled data does not compromise final classification accuracy because it is followed by fine-tuning with a small amount of labeled data, achieving both low annotation costs and high accuracy.
Solution Approach 2:
The system changes the training parameter from requiring labeled data to using unlabeled data for the pre-training phase. By adjusting the training strategy to contrastive learning on unlabeled data followed by fine-tuning, the model achieves robust feature learning without annotation costs, maintaining high classification accuracy.
Data Source
AI summary
Described embodiments relate to methods, systems, and computer-readable media for training a feature encoder for encoding sound samples, such as respiratory sounds. Some embodiments further relate to methods, systems, and computer-readable media for training an audio classifier, such as a respiratory sound classifier, using the pre-trained feature encoder. Some embodiments relate to methods, systems, and computer-readable media for classifying a sample of an audio file, such as a respiratory sound, as being a positive example or a negative example of a condition, such as a respiratory condition.