Random Regression Forests for Acoustic Event Detection and Classification

Acoustic event detection and localization with regression forests

This paper proposes an approach for the efficient automatic joint detection and localization of single-channel acoustic events using random forest regression. The audio signals are decomposed into multiple densely overlapping superframes annotated with event class labels and their displacements to the temporal starting and ending points of the events. Using the displacement information, a multi...

Acoustic Event Detection and Classification

CaR-FOREST: Joint Classification-Regression Decision Forests for Overlapping Audio Event Detection

This report describes our submissions to Task2 and Task3 of the DCASE 2016 challenge [1]. The systems aim at dealing with the detection of overlapping audio events in continuous streams, where the detectors are based on random decision forests. The proposed forests are jointly trained for classification and regression simultaneously. Initially, the training is classification-oriented to encoura...

Pathway analysis using random forests classification and regression

MOTIVATION Although numerous methods have been developed to better capture biological information from microarray data, commonly used single gene-based methods neglect interactions among genes and leave room for other novel approaches. For example, most classification and regression methods for microarray data are based on the whole set of genes and have not made use of pathway information. Pat...

Coupled Sparse Nmf vs. Random Forest Classification for Real Life Acoustic Event Detection

In this paper, we propose two methods for polyphonic Acoustic Event Detection (AED) in real life environments. The first method is based on Coupled Sparse Non-negative Matrix Factorization (CSNMF) of spectral representations and their corresponding class activity annotations. The second method is based on Multi-class Random Forest (MRF) classification of time-frequency patches. We compare the p...