Jorge Martínez

Alexander Quevedo, Abraham Sánchez, Raul Nancláres et al.

The understanding of global climate change, agriculture resilience, and deforestation control rely on the timely observations of the Land Use and Land Cover Change (LULCC). Recently, some deep learning (DL) methods have been adapted to make an automatic classification of Land Cover (LC) for global and homogeneous data. However, most of these DL models can not apply effectively to real-world data. i.e. a large number of classes, multi-seasonal data, diverse climate regions, high imbalance label dataset, and low-spatial resolution. In this work, we present our novel lightweight (only 89k parameters) Convolution Neural Network (ConvNet) to make LC classification and analysis to handle these problems for the Jalisco region. In contrast to the global approaches, the regional data provide the context-specificity that is required for policymakers to plan the land use and management, conservation areas, or ecosystem services. In this work, we combine three real-world open data sources to obtain 13 channels. Our embedded analysis anticipates the limited performance in some classes and gives us the opportunity to group the most similar, as a result, the test accuracy performance increase from 73 % to 83 %. We hope that this research helps other regional groups with limited data sources or computational resources to attain the United Nations Sustainable Development Goal (SDG) concerning Life on Land.

Paul Ralph, Nauman bin Ali, Sebastian Baltes et al.

Empirical Standards are natural-language models of a scientific community's expectations for a specific kind of study (e.g. a questionnaire survey). The ACM SIGSOFT Paper and Peer Review Quality Initiative generated empirical standards for research methods commonly used in software engineering. These living documents, which should be continuously revised to reflect evolving consensus around research best practices, will improve research quality and make peer review more effective, reliable, transparent and fair.

Vincenzo Zaccà, Pablo Martinez-Nuevo, Martin Møller et al.

Accurate sound field reproduction in rooms is often limited by the lack of knowledge of the room characteristics. Information about the room shape or nearby reflecting boundaries can, in principle, be used to improve the accuracy of the reproduction. In this paper, we propose a method to infer the location of nearby reflecting boundaries from measurements on a microphone array. As opposed to traditional methods, we explicitly exploit the loudspeaker directivity model (beyond omnidirectional radiation) and the microphone array geometry. This approach does not require noiseless timing information of the echoes as input, nor a tailored loudspeaker-wall-microphone measurement step. Simulations show the proposed model outperforms current methods that disregard directivity in reverberant environments.