Selected Features for Classifying Environmental Audio Data with Random Forest

Selected Features for Classifying Environmental Audio Data with Random Forest

Environmental audio classification has been the focus in the field of speech recognition. For Environmental audio data, it is difficult to find an optimal classifier and select the optimal features from various features can be extracted. Random forest is a powerful machine learning classifier compared to other conventional pattern recognition techniques. In this paper, the performance of the Ra...

Classifying Music Audio with Timbral and Chroma Features

Music audio classification has most often been addressed bymodeling the statistics of broad spectral features, which, by design, exclude pitch information and reflect mainly instrumentation. We investigate using instead beat-synchronous chroma features, designed to reflect melodic and harmonic content and be invariant to instrumentation. Chroma features are less informative for classes such as ...

Classifying Forest Cover Type using Cartographic Features

Given elevation, hydrologic, soil, and sunlight data can we predict what type of tree would be in a small patch of forest? Our project attempts to predict the predominant type of tree in sections of wooded area. Understanding forest composition is a valuable aspect of managing the health and vitality of our wilderness areas. Classifying cover type can help further research regarding forest fire...

Classifying Human Activities with Temporal Extension of Random Forest

Survival of Dialysis Patients Using Random Survival Forest Model in Low-Dimensional Data with Few-Events

Background:Dialysis is a process for eliminating extra uremic fluids of patients with chronic renal failure. The present study aimed to determine the variables that influence the survival of dialysis patients using random survival forest model (RSFM) in low-dimensional data with low events per variable (EPV). Methods:In this historical cohort study, infor...