Leveraging Pretrained Speech Models and Input-Agnostic Representation-Level Augmentation for Improved Respiratory Sound Classification

核心概念

Exploring the efficacy of pretrained speech models and proposing an input-agnostic representation-level augmentation technique, RepAugment, to improve respiratory sound classification performance.

要約

The paper explores the use of pretrained speech models, such as wav2vec2, HuBERT, and XLS-R, for respiratory sound classification tasks. It finds that while these models demonstrate strong performance on speech-related tasks, there is a characterization gap between speech and respiratory sounds, limiting their effectiveness for lung sound classification.
To address this issue, the authors propose a novel input-agnostic augmentation technique called RepAugment, which operates at the representation level. RepAugment consists of two key components:

Rep-Mask: This operation reduces the model's reliance on particular features by randomly masking out parts of the feature representations.
Rep-Gen: This component expands the model's experience of the underrepresented classes by adding random Gaussian noise to the minority class samples.

The authors demonstrate that RepAugment outperforms the commonly used SpecAugment technique, particularly when applied to models pretrained on speech datasets. Experimental results show that RepAugment achieves state-of-the-art performance on the ICBHI respiratory sound classification dataset, with a score of 61.51% using the AST model and 56.73% using the XLS-R-300M model.
The paper highlights the importance of addressing the distribution gap between speech and respiratory sounds, and the effectiveness of the proposed input-agnostic augmentation approach in bridging this gap and improving the classification of minority disease classes.

統計

"Recent remarkable advances in machine learning are rapidly transforming the landscape of healthcare, offering unprecedented opportunities for early disease detection and personalized treatment pathways."
"To cope with the respiratory data deficiency, previous studies for respiratory sound classification have usually exploited the following two-staged approach: (i) pretraining with sufficient data from other domains (e.g., image or audio data) to enhance general feature representation capability, and (ii) then fine-tuning the pretrained model with data augmentation on respiratory sound to avoid the overfitting issues caused by lack of diverse data."
"Experimental results demonstrate the effectiveness of RepAugment, specifically when applied to models pretrained with audio or speech datasets, outperforming the conventional SpecAugment. Moreover, our approach also shows accuracy gains up to 7.14% for the most under-represented classes, highlighting its potential to improve the diagnostics of abnormal cases."

引用

"Recent advancements in AI have democratized its deployment as a healthcare assistant."
"To cope with the respiratory data deficiency, previous studies for respiratory sound classification have usually exploited the following two-staged approach: (i) pretraining with sufficient data from other domains (e.g., image or audio data) to enhance general feature representation capability, and (ii) then fine-tuning the pretrained model with data augmentation on respiratory sound to avoid the overfitting issues caused by lack of diverse data."
"Experimental results demonstrate the effectiveness of RepAugment, specifically when applied to models pretrained with audio or speech datasets, outperforming the conventional SpecAugment. Moreover, our approach also shows accuracy gains up to 7.14% for the most under-represented classes, highlighting its potential to improve the diagnostics of abnormal cases."

抽出されたキーインサイト

RepAugment: Input-Agnostic Representation-Level Augmentation for Respiratory Sound Classification

by June-Woo Kim... 場所 arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.02996.pdf

深掘り質問

How can the proposed RepAugment technique be extended to other domains beyond respiratory sound classification, such as medical imaging or natural language processing

The RepAugment technique proposed in the context of respiratory sound classification can be extended to other domains such as medical imaging or natural language processing by adapting the augmentation strategy to suit the specific characteristics of the data in those domains. For medical imaging, the representation-level augmentation can involve techniques like feature masking and noise generation applied to the feature maps extracted from imaging models. This can help improve the generalization of pretrained models on medical imaging tasks by introducing variability in the learned representations. In natural language processing, similar augmentation strategies can be applied to the hidden representations of language models to enhance their performance on various NLP tasks. By incorporating RepAugment in these domains, researchers can potentially address data scarcity issues, improve model robustness, and enhance performance on specific tasks.

What are the potential limitations of using pretrained speech models for respiratory sound classification, and how can the characterization gap between speech and respiratory sounds be further reduced

The use of pretrained speech models for respiratory sound classification may face limitations due to the inherent differences between speech and lung sounds. One potential limitation is the domain gap between the data used to train the speech models and the respiratory sound data, leading to suboptimal performance. To reduce this characterization gap, additional strategies can be employed, such as domain adaptation techniques to align the distributions of the data, transfer learning methods to fine-tune the pretrained models on respiratory sound data, and data augmentation approaches like RepAugment to introduce variability and bridge the gap between speech and lung sounds. By addressing these limitations, the performance of pretrained speech models in respiratory sound classification can be enhanced, leading to more accurate and reliable classification results.

Given the importance of addressing class imbalance in medical datasets, how can the insights from this work on representation-level augmentation be leveraged to improve the performance of minority classes in other healthcare applications

Addressing class imbalance in medical datasets is crucial for improving the performance of minority classes in healthcare applications. Insights from the representation-level augmentation techniques like RepAugment can be leveraged to enhance the performance of minority classes by focusing on specific augmentation strategies tailored to these classes. For instance, by applying targeted noise generation or feature masking to the representations of minority classes, the models can learn more robust and discriminative features for these under-represented categories. Additionally, techniques like oversampling, undersampling, or class-specific loss functions can be combined with representation-level augmentation to further boost the performance of minority classes. By integrating these approaches, healthcare applications can achieve more balanced and accurate predictions, especially for rare or under-represented medical conditions.