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Big data and artificial intelligence in earth science: recent progress and future advancements

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Verdu E. , Nieto Y. V. , Saleem N.

2023

tom Vol. 71, no. 3

1373--1375

Earth science informatics has experienced significant growth in recent decades due to advancements in computational efficiency and processing power. The fourth industrial revolution has had a major impact on the environment, making earth science informatics crucial for detecting and predicting changes in the ecosystem and understanding the interactions between the land, ocean, and climate system. To tackle these challenges, data-driven approaches such as big data analytics and artificial intelligence (AI) are being utilized with increasing frequency. Big data analytics and AI allow researchers to analyse and acquire insights more efficiently, making predictions with observed data more effective. AI algorithms learn patterns from input and output data to establish a relationship and create a training model. The optimization of AI algorithms can improve the efficiency of earth science data computation even with complex datasets, uncovering hidden relationships and correlations between data sources. This Special Issue aims to bring together some of the leading experts in the field to share their latest research findings, as well as their perspectives on the future of Big data analytics and AI. Each of the fourteen articles published in this Special Issue has been rigorously peer-reviewed to ensure that they meet the highest standards of quality and scientific rigour. We are pleased to present the following highlights of these carefully selected contributions.

Deep Neural Network for Supervised Single-Channel Speech Enhancement

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Saleem N. , Irfan Khattak M. , Ali M. Y. , Shafi M.

Archives of Acoustics

2019

tom Vol. 44, No. 1

3--12

Speech enhancement is fundamental for various real time speech applications and it is a challenging task in the case of a single channel because practically only one data channel is available. We have proposed a supervised single channel speech enhancement algorithm in this paper based on a deep neural network (DNN) and less aggressive Wiener filtering as additional DNN layer. During the training stage the network learns and predicts the magnitude spectrums of the clean and noise signals from input noisy speech acoustic features. Relative spectral transform-perceptual linear prediction (RASTA-PLP) is used in the proposed method to extract the acoustic features at the frame level. Autoregressive moving average (ARMA) filter is applied to smooth the temporal curves of extracted features. The trained network predicts the coefficients to construct a ratio mask based on mean square error (MSE) objective cost function. The less aggressive Wiener filter is placed as an additional layer on the top of a DNN to produce an enhanced magnitude spectrum. Finally, the noisy speech phase is used to reconstruct the enhanced speech. The experimental results demonstrate that the proposed DNN framework with less aggressive Wiener filtering outperforms the competing speech enhancement methods in terms of the speech quality and intelligibility.