The objective of this thesis is to develop novel classification and feature learning techniques
for the task of sound event detection (SED) in real-world environments. Throughout their …

Rare acoustic event detection, as evidenced by the recent IEEE AASP Challenge on
Detection and Classification of Acoustic Scenes and Events (DCASE 2017), is a growing …

Recently, neural network-based deep learning methods have been popularly applied to
computer vision, speech signal processing and other pattern recognition areas. Remarkable …

Sound event detection (SED) has been widely applied in real world applications.
Convolutional recurrent neural network based SED approaches have achieved state-of-the …

This report presents our audio event detection system submitted for Task 2," Detection of
rare sound events", of DCASE 2017 challenge. The proposed system is based on …

Prompt emergency detection and response in indoor environments is a significant issue due
to the difficulties in detecting indoor emergency events. However, current indoor monitoring …

Abstract Among various Sound Event Detection (SED) systems, Recurrent Neural Networks
(RNN), such as long short-term memory unit and gated recurrent unit, is used to capture …

This paper presents a novel deep learning model called Self-Attention Layer within a
Convolutional Neural Network (SACNN), specifically designed for detecting acoustic data in …

In this paper, we present a sound event detection system based on a deep neural network
(DNN). Exemplar-based noise reduction approach is proposed for enhancing mel-band …

This paper proposes to use low-level spatial features extracted from multichannel audio for
sound event detection. We extend the convolutional recurrent neural network to handle …