Random forest classification based acoustic event detection utilizing contextual-information and bottleneck features

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...

Acoustic Event Detection and Classification

Deep Neural Network Bottleneck Features for Acoustic Event Recognition

Bottleneck features have been shown to be effective in improving the accuracy of speaker recognition, language identification and automatic speech recognition. However, few works have focused on bottleneck features for acoustic event recognition. This paper proposes a novel acoustic event recognition framework using bottleneck features derived from a Deep Neural Network (DNN). In addition to co...

Acoustic Features for Multimedia Event Classification

Because of the popularity of online multimedia videos, there has been much interest in recent years in in multimedia event detection (MED) research. MED requires a system that can search user-submitted quality videos, like those found on YouTube, for specific events. Video features play a significant role is determining the content for MED tasks. However, the audio component for a given video c...

VHR Semantic Labeling by Random Forest Classification and Fusion of Spectral and Spatial Features on Google Earth Engine

Semantic labeling is an active field in remote sensing applications. Although handling high detailed objects in Very High Resolution (VHR) optical image and VHR Digital Surface Model (DSM) is a challenging task, it can improve the accuracy of semantic labeling methods. In this paper, a semantic labeling method is proposed by fusion of optical and normalized DSM data. Spectral and spatial featur...