A Novel Scheme of Speech Enhancement using Power Spectral Subtraction - Multi-Layer Perceptron Network
Abstract: A novel method for
eliminating noise from a noised speech signal in order to improve its quality using
combined power spectral subtraction and multi-layer perceptron network is
presented in this paper. Firstly, the contaminated speech signal was processed
by spectral subtraction to enhance the clean speech signal. Then, the signal
was processed by a neural network using the spectral subtraction parameters and
result of estimated speech signal in order to improve its signal quality and
intelligibility. The artificial neural network used was multi-layer perceptron
network consisted of three layers with six input and one output. The neural
network was trained with three speech signals contaminated with two level white
gaussian noises in SNR including 0 dB and 30dB. The designed speech enhancement
was examined with ten noised speech signals. Based on the experiments, the
improvement of signal quality SNR was up to 7 dB when the signal quality input
was 0dB. Then, based on the PESQ score, the proposed method can improve up to
0.4 from its origin value. Those experiment results show that the proposed
method is capable to improve both the signal quality and intelligibility better
than the original power spectral subtraction.
Keywords: speech enhancement,
spectral subtraction, artificial neural network, multi-layer perceptron
Author: Budiman P.A. Rohman,
Ken Paramayudha, Asep Yudi Hercuadi
Journal Code: jptkomputergg160171
