Taking advantage of the latest advances in deep learning for seismology, we address earthquake characterization from a data-driven perspective. Many of the usual procedures for extracting information from seismograms require processing a large volume of data using empirical and physics rule-based techniques. In this letter, we propose a novel approach for estimating epicentral distance, depth, and magnitude directly from individual raw three-component seismograms of 1-min length observed by single stations. Our convolutional neural network-based method is able to handle complex-valued representations of the seismic data in the time-frequency domain by using dedicated convolutional and activation functions. In this way, our method benefits both from extracting relevant information through time-frequency domain analysis and from designing a single architecture that deals with complex information. The proposed method achieves a mean absolute error of 4.51 km for epicentral distance, 6.15 km for depth, and 0.26 for magnitude estimation. The experiments were conducted over a publicly available and large database, STanford EArthquake data set (STEAD), and the comparisons with current state-of-the-art approaches show the effectiveness of the proposed approach. Source code and best model are available at https://github.com/ristea/stead-earthquake-cnn.

Humans express and perceive emotions in a multimodal manner. The multimodal information is intrinsically fused by the human sensory system in a complex manner. Emulating a temporal desynchronisation between modalities, in this paper, we design an end-to-end neural network architecture, called TA-AVN, that aggregates temporal audio and video information in an asynchronous setting in order to determine the emotional state of a subject. The feature descriptors for audio and video representations are extracted using simple Convolutional Neural Networks (CNNs), leading to real-time processing. Undoubtedly, collecting annotated training data remains an important challenge when training emotion recognition systems, both in terms of effort and expertise required. The proposed approach solves this problem by providing a natural augmentation technique that allows achieving a high accuracy rate even when the amount of annotated training data is limited. The framework is tested on three challenging multimodal reference datasets for the emotion recognition task, namely the benchmark datasets CREMA-D and RAVDESS, and one dataset from the FG2020’s challenge related to emotion recognition. The results prove the effectiveness of our approach and our end-to-end framework achieves state-of-the-art results on the CREMA-D and RAVDESS datasets.

In this letter, we present a novel technique for unsupervised change analysis that leads to a method of ranking the changes that occur between two satellite images acquired at different moments of time. The proposed change analysis is based on binary descriptors and uses the Hamming distance as a similarity metric. In order to render a completely unsupervised solution, the obtained distances are further classified using vector quantization methods (i.e., Lloyd's algorithm for optimal quantization). The ultimate goal in the change analysis chain is to build change intensity maps that provide an overview of the severeness of changes in the area under analysis. In addition, the proposed analysis technique can be easily adapted for change detection by selecting only two levels for quantization. This discriminative method (i.e., between changed/unchanged zones) is compared with other previously developed techniques that use principal component analysis or Bayes theory as starting points for their analysis. The experiments are carried on Landsat images at a 30-m spatial resolution, covering an area of approximately 59×51 km ² over the surroundings of Bucharest, Romania, and containing multispectral information.

Emotion recognition is a key research topic in the Affective Computing domain, with implications in marketing, human-robot interaction, and health domains. The continuous technological advances in terms of sensors and the rapid development of artificial intelligence technologies led to breakthroughs and improved the interpretation of human emotions. In this paper, we propose a lightweight neural network architecture that extracts and performs the analysis of multimodal information using the same audio and visual networks across multiple temporal segments. Undoubtedly, data collection and annotation for emotion recognition tasks remain challenging aspects in terms of required expertise and effort spent. In this sense, the learning process of the proposed multimodal architecture is based on an iterative procedure that starts with a small volume of annotated samples and allows a step-by-step improvement of the system by assessing the model uncertainty in recognizing discrete emotions. Specifically, at each epoch, the learning process is guided by the most uncertainly annotated samples and integrates different modes of expressing emotions through a simple augmentation technique. The framework is tested on two publicly available multimodal datasets for emotion recognition, i.e. CREMA-D and RAVDESS, using 5-folds cross-validation. Compared to state-of-the-art methods, the achieved performance demonstrates the effectiveness of the proposed approach, with an overall accuracy of 74.2 % on CREMA-D and 76.3 % on RAVDESS. Moreover, with a small number of model parameters and a low inference time, the proposed neural network architecture represents a valid candidate for the integration on platforms with limited memory and computational resources.

This paper presents a novel framework for multilabel classification of multispectral remote sensing images using error-correcting output codes. Starting with a set of primary class labels, the proposed framework consists in transforming the multiclass problem into multiple binary learning subtasks. The distributed output representations of these binary learners are then transformed into primary class labels. In order to train robust binary classifiers on a reduced annotated dataset, the learning process is iterative and involves determining most ambiguous examples, which are included in the training set at each iteration. As part of the semantic image recognition process, two categories of high-level image representations are proposed for the feature extraction part. First, deep convolutional neural networks are used to form high-level representations of the images. Second, we test our classification framework with a bag-of-visual words model based on the scale invariant feature transform, used in combination with color descriptors. In the first case, we propose the usage of pretrained state-of-the-art deep learning models that cancel the need to estimate model parameters of complex architectures, whereas, in the second case, a dictionary of visual words must be determined from the training set. Experiments are conducted on GeoEye-1 and Sentinel-2 images and the results show the effectiveness of the proposed approach toward a multilabel classification, when compared to other methods.

The current technological developments lead to increased heterogeneity and variability in remote sensing imagery. In this context, unsupervised multimodal change detection techniques are mandatory to perform a continuous monitoring and rapid damage assessment by means of heterogeneous remote sensing data. Taking advantage of the latest advances in deep learning, we address multimodal change detection from an intermodality image translation perspective. Intermodality translation is achieved by means of generative adversarial networks (GANs) built over U-Net architectures at both generator and discriminator levels and trained under CutMix transformations. A change prior is used to guide the learning process of the neural network framework and to reduce the impact of changed locations over the learned model. The change prior is derived in an unsupervised manner from comparisons between the postevent locations and $k$ nearest neighbor ( $k$ NN) locations determined in the preevent image. The experiments were conducted over several pairs of heterogeneous remote sensing images, and the comparisons with current state-of-the-art approaches show the effectiveness of the proposed multimodal change detection framework.