Search Results for author: David Chesmore

Found 7 papers, 7 papers with code

Environmental Sound Recognition using Masked Conditional Neural Networks

1 code implementation8 Apr 2018 Fady Medhat, David Chesmore, John Robinson

Neural network based architectures used for sound recognition are usually adapted from other application domains, which may not harness sound related properties.

Masked Conditional Neural Networks for Audio Classification

1 code implementation6 Mar 2018 Fady Medhat, David Chesmore, John Robinson

We present the ConditionaL Neural Network (CLNN) and the Masked ConditionaL Neural Network (MCLNN) designed for temporal signal recognition.

Audio Classification General Classification

Music Genre Classification using Masked Conditional Neural Networks

1 code implementation18 Feb 2018 Fady Medhat, David Chesmore, John Robinson

MCLNN has achieved accuracies that are competitive to state-of-the-art handcrafted attempts in addition to models based on Convolutional Neural Networks.

Classification General Classification +2

Masked Conditional Neural Networks for Automatic Sound Events Recognition

1 code implementation15 Feb 2018 Fady Medhat, David Chesmore, John Robinson

Deep neural network architectures designed for application domains other than sound, especially image recognition, may not optimally harness the time-frequency representation when adapted to the sound recognition problem.

Recognition of Acoustic Events Using Masked Conditional Neural Networks

1 code implementation7 Feb 2018 Fady Medhat, David Chesmore, John Robinson

Automatic feature extraction using neural networks has accomplished remarkable success for images, but for sound recognition, these models are usually modified to fit the nature of the multi-dimensional temporal representation of the audio signal in spectrograms.

Automatic Classification of Music Genre using Masked Conditional Neural Networks

1 code implementation16 Jan 2018 Fady Medhat, David Chesmore, John Robinson

Neural network based architectures used for sound recognition are usually adapted from other application domains such as image recognition, which may not harness the time-frequency representation of a signal.

Classification General Classification

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