Noise Test Set Research Articles

A multi-task learning network based on the Transformer network for airborne electromagnetic detection imaging and denoising

Abstract As an emerging geophysical exploration technology in recent years, airborne electromagnetic exploration has the advantages of adapting to diverse terrains, wide coverage, and providing a large amount of electromagnetic data, and can be applied to the rapid collection of large amounts of data. Scenarios are often used in fields such as deep geological structures, mineral resource exploration, and environmental engineering research. However, traditional airborne electromagnetic data inversion technology usually takes a long time to process a large amount of airborne electromagnetic data, and it is difficult to remove the noise in the later signals. Therefore, this paper proposes a multi-task learning network structure based on Transformer. By constraining the two network branches of imaging and denoising, a sub-network with simultaneous denoising and imaging is established to process aeronautical electromagnetic data. The noise test set is introduced for testing. This model achieved a 582.61% signal-to-noise ratio improvement in smooth Gaussian noise denoising, and a 129.69% and 112.74% signal-to-noise ratio improvement in non-smooth Gaussian noise and random impulse noise denoising, respectively. The method proposed in this article overcomes the shortcomings of traditional inversion imaging such as slow speed and low resolution, and at the same time eliminates the influence of noise in airborne electromagnetic data. This is of great significance for the application of deep learning in the field of geophysical exploration.

PLDE: A lightweight pooling layer for spoken language recognition

In recent years, the transfer learning method of replacing acoustic features with phonetic features has become a new paradigm for end-to-end spoken language recognition. However, these larger transfer learning models always encode too much redundant information. In this paper, we propose a lightweight language recognition decoder based on a phonetic learnable dictionary encoding (PLDE) layer, which is more suitable for phonetic features and achieves better recognition performances while significantly reducing the number of parameters. The lightweight decoder consists of three main parts: (1) a phonetic learnable dictionary with ghost clusters, which improves the traditional LDE pooling layer and enhances the model’s ability to model noise with ghost clusters; (2) coarse-grained chunk-level pooling, which can highlight the phone sequence and suppress noise around ghost clusters, and hence reduce their influence to the subsequent network; (3) fine-grained chunk-level projection, which enables the discriminative network to obtain more linguistic information and hence improve the model’s modelling ability. These three parts simplify the language recognition decoder into a PLDE pooling layer, reducing the parameter size of the decoder by at least one order of magnitude while achieving better recognition performances. In experiments on the OLR2020 dataset, the Cavg of the proposed method exceeds that of the current state-of-the-art language recognition system, achieving 24.68% and 42.24% improvements on the cross-channel test set and unknown noise test set, respectively. Furthermore, experimental results on the OLR2021 dataset also demonstrate the effectiveness of PLDE.

Classification of mechanisms underlying cardiac arrhythmias by deep learning

This work presents the development and comparison of classifiers based on both traditional classifiers and more advanced deep learning techniques to identify different types of bioelectric propagation in cardiac tissue, which are the mechanisms underlying arrhythmias. The classification uses energy maps obtained from the magnetic field generated by three possible bioelectric models of propagations through simulated cardiac cells (classes), consisting of a linear path, a reentrant circuit around an inexcitable obstacle, and a spiral trajectory. Different classifiers were implemented and compared in regard to their accuracies, aiming to associate each energy map to its respective class. These classifiers were evaluated both on a test set composed by images without noise and on a more challenging “noise test set,” composed by images meant to test the generalization powers of the models in the presence of noise. A Monte Carlo cross-validation approach was used to evaluate the averaged behavior of the classifiers over 10 runs. The classifiers compared include conventional decision support tools as decision trees and random forests, a simple multi-layer perceptron, and deep learning architectures such as a simple Convolutional Neural Network, ResNet50, and InceptionV3. These last two had the best performances on the noise test set, with averaged accuracies of 0.852 and 0.827 over 10 runs, and standard deviations of 0.036 and 0.048, respectively. The use of handcrafted features, specifically histograms of oriented gradients, was also shown to substantially improve results for the classifiers that do not use convolutions, to the point of surpassing the results of the deeper neural networks. The comparative performance of the studied classifiers elucidates some of the properties of this classification problem. ResNet50 and InceptionV3 seem to be promising candidate models for initial tests with real-life data. The study of handcrafted features might also prove useful, especially if the availability of real images is scarce. The use of this automated classification may contribute to the efficacy of diagnostics in a future use of biomagnetic mappings in a medical-hospital environment.

Extending the Frequency Range of Digital Noise Measurements to the Microwave Domain

We describe the use of digital phase noise test sets at frequencies well beyond the sampling rate of their analog-to-digital converters. The technique proposed involves the transfer of phase fluctuations from an arbitrary high carrier frequency to within the operating frequency range of the digital instrument. The validity of the proposed technique has been proven via comparison with conventional methods. Digital noise measurements eliminate the need for calibration and improve consistency of experimental results. Mechanisms limiting the resolution of spectral measurements are also discussed.