Abstract: The goal of the IEEE 2018 FEMH Voice Data Challenge was to develop an effective algorithmic approach to classifying voice samples as normal or ...

The training dataset includes 50 normal voice samples and 150 samples of common voice disorders, including vocal nodules, polyps, and cysts. (60 samples); ...

This approach was able to achieve a sensitivity of 89% and specificity of 76% in classifying normal from pathological samples and an unweighted average recall ( ...

A Multi-Representation Ensemble Approach to Classifying Vocal Diseases ... classifies voice signals stemming from four types of voice disorders. To this ...

Jun 23, 2024 · Analyzing and identifying structures within medical images or data about the VF data is the primary goal of VF classification and segmentation.

A multi-representation ensemble approach to classifying vocal diseases. M Ju, Z Jiang, Y Chen, S Ray. 2018 IEEE International Conference on Big Data (Big Data) ...

Oct 25, 2023 · Ju, M., Jiang, Z., Chen, Y., et al., “A multi-representation ensemble approach to classifying vocal diseases,” in 2018 IEEE International ...

A Multi-representation Ensemble Approach to Classifying Vocal Diseases M. Ju, Z. Jiang, Y. Chen, S. Ray Big Data 18 (3rd place award for FEMH Challenge) ...

Ray, "A Multi-Representation Ensemble Approach to Classifying Vocal Diseases," 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA ...

The proposed VoiceLens system develops the voice-based classification model that may identify disease-specific pathology conditions from both sustained ...