The automatic recognition of sound events by computers is an important aspect of emerging
applications such as automated surveillance, machine hearing and auditory scene …

We present in this paper a simple, yet efficient convolutional neural network (CNN)
architecture for robust audio event recognition. Opposing to deep CNN architectures with …

The ability to automatically recognize a wide range of sound events in real-world conditions
is an important part of applications such as acoustic surveillance and machine hearing. Our …

The objective of this research is to develop feature extraction and classification techniques
for the task of sound event recognition (SER) in unstructured environments. Although this …

The aim of this work is to utilize both audio and visual speech information to create a robust
voice activity detector (VAD) that operates in both clean and noisy speech. A statistical …

In this paper, we introduce the system developed at the Institute for Infocomm Research (I2
R) for the ASpIRE (Automatic Speech recognition In Reverberant Environments) challenge …

The aim of this work is to examine the correlation between audio and visual speech features.
The motivation is to find visual features that can provide clean audio feature estimates which …

This work proposes a novel method of predicting formant frequencies from a stream of mel-
frequency cepstral coefficients (MFCC) feature vectors. Prediction is based on modelling the …

The aim of this work is to investigate a selection of audio and visual speech features with the
aim of finding pairs that maximise audio-visual correlation. Two audio speech features have …

This work begins by examining the correlation between audio and visual speech features
and reveals higher correlation to exist within individual phoneme sounds rather than …