Removing Noise from Speech Signals Using Different Approaches of Artificial Neural Networks

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Omaima N. A. AL-Allaf 1,*

1. Faculty of Sciences & IT, Dept. of Basic Sciences, Al-Zaytoonah University of Jordan, P.O. Box130, Amman (11733), Jordan

* Corresponding author.


Received: 4 Sep. 2014 / Revised: 10 Jan. 2015 / Accepted: 25 Mar. 2015 / Published: 8 Jun. 2015

Index Terms

Signal Enhancement, Artificial Neural Networks, Function Fitting (FitNet), Nonlinear AutoRegressive (NARX), Recurrent (RNNs), and Cascaded-ForwardNet


In this research, four ANN models: Function Fitting (FitNet), Nonlinear AutoRegressive (NARX), Recurrent (RNNs), and Cascaded-ForwardNet were constructed and trained separately to become a filter to remove noise from any speech signal. Each model consists of input, hidden and output layers. Two neurons in the input layer that represent speech signal and its associated noise. The output layer includes one neuron that represent the enhanced signal after removing noise. The four models were trained separately on stereo (noisy and clean) audio signals to produce the clean signal. Experiments were conducted for each model separately with different: architecture; optimization training algorithms; and learning parameters to identify model with best results of removing noise from speech signal. From experiments, best results were obtained from FitNet and NARAX models respectively. TrainLM is the best training algorithm in this case. Finally, the results showed that the suggested architecture of the four models have filtering ability to remove noise form both trained and not trained speech signals samples.

Cite This Paper

Omaima N. A. AL-Allaf, "Removing Noise from Speech Signals Using Different Approaches of Artificial Neural Networks", International Journal of Information Technology and Computer Science(IJITCS), vol.7, no.7, pp.8-18, 2015. DOI:10.5815/ijitcs.2015.07.02


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