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Abstract:In the last few years, due to the technological improvement of advanced metering infrastructures, water and natural gas grids can be regarded as smart-grids, similarly to power ones. However, considering the number of studies related to the application of computational intelligence to distribution grids, the gap between power grids and water/gas grids is notably wide. For this purpose, in this paper, a framework for leakage identification is presented. The framework is composed of three sections aimed at the extraction and the selection of features and at the detection of leakages. A variation of the Sequential Feature Selection (SFS) algorithm is used to select the best performing features within a set, including, also, innovative temporal ones. The leakage identification is based on novelty detection and exploits the characterization of a normality model. Three statistical approaches, The Gaussian Mixture Model (GMM), Hidden Markov Model (HMM) and One-Class Support Vector Machine (OC-SVM), are adopted, under a comparative perspective. Both residential and office building environments are investigated by means of two datasets. One is the Almanac of Minutely Power dataset (AMPds), and it provides water and gas data consumption at 1, 10 and 30 min of time resolution; the other is the Department of International Development (DFID) dataset, and it provides water and gas data consumption at 30 min of time resolution. The achieved performance, computed by means of the Area Under the Curve (AUC), reaches 90% in the office building case study, thus confirming the suitability of the proposed approach for applications in smart water and gas grids.

Cry detection is an important facility in both residential and public environments, which can answer to different needs of both private and professional users. In this paper, we investigate the problem of cry detection in professional environments, such as Neonatal Intensive Care Units (NICUs). The aim of our work is to propose a cry detection method based on deep neural networks (DNNs) and also to evaluate whether a properly designed synthetic dataset can replace on-field acquired data for training the DNN-based cry detector. In this way, a massive data collection campaign in NICUs can be avoided, and the cry detector can be easily retargeted to different NICUs. The paper presents different solutions based on single-channel and multi-channel DNNs. The experimental evaluation is conducted on the synthetic dataset created by simulating the acoustic scene of a real NICU, and on a real dataset containing audio acquired on the same NICU. The evaluation revealed that using real data in the training phase allows achieving the overall highest performance, with an Area Under Precision-Recall Curve (PRC-AUC) equal to 87.28 %, when signals are processed with a beamformer and a post-filter and a single-channel DNN is used. The same method, however, reduces the performance to 70.61 % when training is performed on the synthetic dataset. On the contrary, under the same conditions, the new single-channel architecture introduced in this paper achieves the highest performance with a PRC-AUC equal to 80.48 %, thus proving that the acoustic scene simulation strategy can be used to train a cry detection method with positive results. INDEX TERMS Infant cry detection, deep neural networks, neonatal intensive care unit, data augmentation, acoustic scene simulation, computational audio processing.