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- Exploring Pre-trained General-purpose Audio Representations for Heart . . .
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
- Exploring Pre-trained General-purpose Audio Representations for Heart . . .
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
- Exploring finetuned audio-LLM on heart murmur features
Our findings highlight the ability of audio LLMs to capture nuanced cardiac characteristics, offering valuable support for cardiologists in diagnosing heart conditions with greater precision
- Exploring Pre-trained General-purpose Audio Representations for Heart . . .
The currently largest pediatric heart sound dataset has been prepared, and for the first time, each cardiac murmur has been manually annotated by an expert annotator according to its timing, shape, pitch, grading, and quality
- Exploring Pre-trained General-purpose Audio Representations . . .
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
- Exploring Pre-trained General-purpose Audio Representations for Heart . . .
On the contrary, many pre-trained models for general audio tasks are available as general-purpose audio represen-tations This study explores the potential of general-purpose audio representations pre-trained on large-scale datasets for transfer learning in heart murmur detection
- Exploring Pre-trained General-purpose Audio Representations for Heart . . .
Pre-trained models are essential as feature extractors in modern machine learning systems in various domains In this study, we hypothesize that representations effective for general
- Exploring Wav2vec 2. 0 Model for Heart Sound Analysis
This framework is evaluated for heart murmur detection or segmentation of S1 and S2 sounds Even though our pre-trained model was trained on a smaller dataset with 52 hours of audio, its performance is slightly better than the model pre-trained on large corpus of 960 hours for speech representation
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