Public articles linked to the same research event.
Physiological Measurement This work proposes a novel architecture that integrates a local feature extraction module with a global context model, in which the Mel_Grouper module serves as a front-end to enhance local pathological representations and its output is fed into the Transformer-based Mel_Encoder to fuse global context, achieving improvements of 3.37%, 3.06%, 6.83% and 5.36% over the previous best results on the four subtasks of the SJTU Paediatric Respiratory Sound (SPRSound) dataset, with further experiments on a real-world paediatric respiratory sound dataset.
This work proposes a novel architecture that integrates a local feature extraction module with a global context model, in which the Mel_Grouper module serves as a front-end to enhance local pathological representations and its output is fed into the Transformer-based Mel_Encoder to fuse global context, achieving improvements of 3.37%, 3.06%, 6.83% and 5.36% over the previous best results on the four subtasks of the SJTU Paediatric Respiratory Sound (SPRSound) dataset, with further experiments on a real-world paediatric respiratory sound dataset.
This work proposes a novel architecture that integrates a local feature extraction module with a global context model, in which the Mel_Grouper module serves as a front-end to enhance local pathological representations and its output is fed into the Transformer-based Mel_Encoder to fuse global context, achieving improvements of 3.37%, 3.06%, 6.83% and 5.36% over the previous best results on the four subtasks of the SJTU Paediatric Respiratory Sound (SPRSound) dataset, with further experiments on a real-world paediatric respiratory sound dataset.
This work proposes a novel architecture that integrates a local feature extraction module with a global context model, in which the Mel_Grouper module serves as a front-end to enhance local pathological representations and its output is fed into the Transformer-based Mel_Encoder to fuse global context, achieving improvements of 3.37%, 3.06%, 6.83% and 5.36% over the previous best results on the four subtasks of the SJTU Paediatric Respiratory Sound (SPRSound) dataset, with further experiments on a real-world paediatric respiratory sound dataset.