Visualising large-scale neural network models in real-time

This article investigates the use of deep neural networks for black-box modelling of audio distortion circuits, such as guitar amplifiers and distortion pedals. Both a feedforward network, based on the WaveNet model, and a recurrent neural network model are compared. To determine a suitable hyperparameter configuration for the WaveNet, models of three popular audio distortion pedals were created: the Ibanez Tube Screamer, the Boss DS-1, and the Electro-Harmonix Big Muff Pi. It is also shown that three minutes of audio data is sufficient for training the neural network models. Real-time implementations of the neural networks were used to measure their computational load. To further validate the results, models of two valve amplifiers, the Blackstar HT-5 Metal and the Mesa Boogie 5:50 Plus, were created, and subjective tests were conducted. The listening test results show that the models of the first amplifier could be identified as different from the reference, but the sound quality of the best models was judged to be excellent. In the case of the second guitar amplifier, many listeners were unable to hear the difference between the reference signal and the signals produced with the two largest neural network models. This study demonstrates that the neural network models can convincingly emulate highly nonlinear audio distortion circuits, whilst running in real-time, with some models requiring only a relatively small amount of processing power to run on a modern desktop computer.

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Evaluation of Pre-Trained Convolutional Neural Network Models for Object Recognition

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.15.17509 ◽

2018 ◽

Vol 7 (3.15) ◽

pp. 95 ◽

Cited By ~ 1

Author(s):

M Zabir ◽

N Fazira ◽

Zaidah Ibrahim ◽

Nurbaity Sabri

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Large Scale ◽

Visual Recognition ◽

Error Function ◽

Network Models ◽

Neural Network Models ◽

California Institute Of Technology ◽

Institute Of Technology ◽

Similar Accuracy

This paper aims to evaluate the accuracy performance of pre-trained Convolutional Neural Network (CNN) models, namely AlexNet and GoogLeNet accompanied by one custom CNN. AlexNet and GoogLeNet have been proven for their good capabilities as these network models had entered ImageNet Large Scale Visual Recognition Challenge (ILSVRC) and produce relatively good results. The evaluation results in this research are based on the accuracy, loss and time taken of the training and validation processes. The dataset used is Caltech101 by California Institute of Technology (Caltech) that contains 101 object categories. The result reveals that custom CNN architecture produces 91.05% accuracy whereas AlexNet and GoogLeNet achieve similar accuracy which is 99.65%. GoogLeNet consistency arrives at an early training stage and provides minimum error function compared to the other two models.

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Data-to-Text Generation with Content Selection and Planning

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33016908 ◽

2019 ◽

Vol 33 ◽

pp. 6908-6915 ◽

Cited By ~ 5

Author(s):

Ratish Puduppully ◽

Li Dong ◽

Mirella Lapata

Keyword(s):

Neural Network ◽

Network Architecture ◽

Large Scale ◽

Network Models ◽

Text Generation ◽

Neural Network Models ◽

Generation Task ◽

Content Selection ◽

End To End ◽

Two Stages

Recent advances in data-to-text generation have led to the use of large-scale datasets and neural network models which are trained end-to-end, without explicitly modeling what to say and in what order. In this work, we present a neural network architecture which incorporates content selection and planning without sacrificing end-to-end training. We decompose the generation task into two stages. Given a corpus of data records (paired with descriptive documents), we first generate a content plan highlighting which information should be mentioned and in which order and then generate the document while taking the content plan into account. Automatic and human-based evaluation experiments show that our model1 outperforms strong baselines improving the state-of-the-art on the recently released RotoWIRE dataset.

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