Audiovisual Tracking of Multiple Speakers in Smart Spaces.

This paper presents GAVT, a highly accurate audiovisual 3D tracking system based on particle filters and a probabilistic framework, employing a single camera and a microphone array. Our first contribution is a complex visual appearance model that accurately locates the speaker's mouth. It transforms a Viola & Jones face detector classifier kernel into a likelihood estimator, leveraging knowledge from multiple classifiers trained for different face poses. Additionally, we propose a mechanism to handle occlusions based on the new likelihood's dispersion. The audio localization proposal utilizes a probabilistic steered response power, representing cross-correlation functions as Gaussian mixture models. Moreover, to prevent tracker interference, we introduce a novel mechanism for associating Gaussians with speakers. The evaluation is carried out using the AV16.3 and CAV3D databases for Single- and Multiple-Object Tracking tasks (SOT and MOT, respectively). GAVT significantly improves the localization performance over audio-only and video-only modalities, with up to 50.3% average relative improvement in 3D when compared with the video-only modality. When compared to the state of the art, our audiovisual system achieves up to 69.7% average relative improvement for the SOT and MOT tasks in the AV16.3 dataset (2D comparison), and up to 18.1% average relative improvement in the MOT task for the CAV3D dataset (3D comparison).

Segregating the true perturbation position from ghost energy points region in ϕ-OTDR systems.

ϕ-OTDR perturbation detection applications demand optimal precision of the perturbation location. Strategies for improving both signal-to-noise (SNR) and precision of the perturbation location in a laboratory environment may fail when applying to a very long fiber under test (FUT) in real-field environments. With this deployment, meaningful energy points representing the response of a certain perturbation can be located at random locations of the fiber other than the original location of the perturbation. These random locations are referred to as the ghost energy points that confuse the system to mistakenly consider the location of these points as the original perturbation location. We present in this paper a novel space-time scanning (ST-scan) method that segregates the ghost energy point locations from those of the real perturbation so that the original perturbation location estimation is improved.

Towards End-to-End Acoustic Localization Using Deep Learning: From Audio Signals to Source Position Coordinates.

This paper presents a novel approach for indoor acoustic source localization using microphone arrays, based on a Convolutional Neural Network (CNN). In the proposed solution, the CNN is designed to directly estimate the three-dimensional position of a single acoustic source using the raw audio signal as the input information and avoiding the use of hand-crafted audio features. Given the limited amount of available localization data, we propose, in this paper, a training strategy based on two steps. We first train our network using semi-synthetic data generated from close talk speech recordings. We simulate the time delays and distortion suffered in the signal that propagate from the source to the array of microphones. We then fine tune this network using a small amount of real data. Our experimental results, evaluated on a publicly available dataset recorded in a real room, show that this approach is able to produce networks that significantly improve existing localization methods based on SRP-PHAT strategies and also those presented in very recent proposals based on Convolutional Recurrent Neural Networks (CRNN). In addition, our experiments show that the performance of our CNN method does not show a relevant dependency on the speaker's gender, nor on the size of the signal window being used.

Headgear Accessories Classification Using an Overhead Depth Sensor.

In this paper, we address the generation of semantic labels describing the headgear accessories carried out by people in a scene under surveillance, only using depth information obtained from a Time-of-Flight (ToF) camera placed in an overhead position. We propose a new method for headgear accessories classification based on the design of a robust processing strategy that includes the estimation of a meaningful feature vector that provides the relevant information about the people's head and shoulder areas. This paper includes a detailed description of the proposed algorithmic approach, and the results obtained in tests with persons with and without headgear accessories, and with different types of hats and caps. In order to evaluate the proposal, a wide experimental validation has been carried out on a fully labeled database (that has been made available to the scientific community), including a broad variety of people and headgear accessories. For the validation, three different levels of detail have been defined, considering a different number of classes: the first level only includes two classes (hat/cap, and no hat/cap), the second one considers three classes (hat, cap and no hat/cap), and the last one includes the full class set with the five classes (no hat/cap, cap, small size hat, medium size hat, and large size hat). The achieved performance is satisfactory in every case: the average classification rates for the first level reaches 95.25%, for the second one is 92.34%, and for the full class set equals 84.60%. In addition, the online stage processing time is 5.75 ms per frame in a standard PC, thus allowing for real-time operation.

A Novel Fiber Optic Based Surveillance System for Prevention of Pipeline Integrity Threats.

This paper presents a novel surveillance system aimed at the detection and classification of threats in the vicinity of a long gas pipeline. The sensing system is based on phase-sensitive optical time domain reflectometry (ϕ-OTDR) technology for signal acquisition and pattern recognition strategies for threat identification. The proposal incorporates contextual information at the feature level and applies a system combination strategy for pattern classification. The contextual information at the feature level is based on the tandem approach (using feature representations produced by discriminatively-trained multi-layer perceptrons) by employing feature vectors that spread different temporal contexts. The system combination strategy is based on a posterior combination of likelihoods computed from different pattern classification processes. The system operates in two different modes: (1) machine + activity identification, which recognizes the activity being carried out by a certain machine, and (2) threat detection, aimed at detecting threats no matter what the real activity being conducted is. In comparison with a previous system based on the same rigorous experimental setup, the results show that the system combination from the contextual feature information improves the results for each individual class in both operational modes, as well as the overall classification accuracy, with statistically-significant improvements.

Comparing the Performance of Indoor Localization Systems through the EvAAL Framework.

In recent years, indoor localization systems have been the object of significant research activity and of growing interest for their great expected social impact and their impressive business potential. Application areas include tracking and navigation, activity monitoring, personalized advertising, Active and Assisted Living (AAL), traceability, Internet of Things (IoT) networks, and Home-land Security. In spite of the numerous research advances and the great industrial interest, no canned solutions have yet been defined. The diversity and heterogeneity of applications, scenarios, sensor and user requirements, make it difficult to create uniform solutions. From that diverse reality, a main problem is derived that consists in the lack of a consensus both in terms of the metrics and the procedures used to measure the performance of the different indoor localization and navigation proposals. This paper introduces the general lines of the EvAAL benchmarking framework, which is aimed at a fair comparison of indoor positioning systems through a challenging competition under complex, realistic conditions. To evaluate the framework capabilities, we show how it was used in the 2016 Indoor Positioning and Indoor Navigation (IPIN) Competition. The 2016 IPIN competition considered three different scenario dimensions, with a variety of use cases: (1) pedestrian versus robotic navigation, (2) smartphones versus custom hardware usage and (3) real-time positioning versus off-line post-processing. A total of four competition tracks were evaluated under the same EvAAL benchmark framework in order to validate its potential to become a standard for evaluating indoor localization solutions. The experience gained during the competition and feedback from track organizers and competitors showed that the EvAAL framework is flexible enough to successfully fit the very different tracks and appears adequate to compare indoor positioning systems.

Source localization with acoustic sensor arrays using generative model based fitting with sparse constraints.

This paper presents a novel approach for indoor acoustic source localization using sensor arrays. The proposed solution starts by defining a generative model, designed to explain the acoustic power maps obtained by Steered Response Power (SRP) strategies. An optimization approach is then proposed to fit the model to real input SRP data and estimate the position of the acoustic source. Adequately fitting the model to real SRP data, where noise and other unmodelled effects distort the ideal signal, is the core contribution of the paper. Two basic strategies in the optimization are proposed. First, sparse constraints in the parameters of the model are included, enforcing the number of simultaneous active sources to be limited. Second, subspace analysis is used to filter out portions of the input signal that cannot be explained by the model. Experimental results on a realistic speech database show statistically significant localization error reductions of up to 30% when compared with the SRP-PHAT strategies.

[Mean length of stay and prognosis in unstable angina. Results from the ARIAM database]. / Estancia media y pronóstico en la angina inestable. Resultados del registro ARIAM.

BACKGROUND AND OBJECTIVE: The objective of this project is to investigate the factors predicting mortality and mean length of stay in patients diagnosed with unstable angina (UA) during admission to the Intensive Care Unit or Critical Care Unit (ICU/CCU). PATIENTS AND METHOD: A retrospective cohort study including all the UA patients listed in the Spanish ARIAM register. The study period comprised from June, 1996 to December, 2003. The follow-up period is limited to the stay in the ICU/CCU. One univariate analysis was performed between deceased and live patients; and another between prolonged and non-prolonged stay patients. Three multivariate analyses were also performed; one to evaluate the factors related to mortality, another to evaluate the variables associated to percutaneous coronary intervention (PCI) and another to evaluate the factors associated to the prolonged mean stay in ICU/CCU. RESULTS: 14,096 patients with UA were included in the study. The UA mortality rate during ICU/CCU admission was 1.1%. Mortality was associated to Killip classification, age, the need for CPR, development of cardiogenic shock, development of arrhythmia (such as VF, sinus tachycardia or high-degree atrioventricular block) and diabetes; whereas patients who smoke were associated to a lower mortality rate. PCI was only performed in 1,226 patients (8.9%), increasing over the years. The PCI-predicting variables were: age, being referred from another hospital, smoking, presenting prior acute myocardial infarction (AMI), complications consisting of cardiogenic shock or high-degree atrioventricular block and being treated with oral beta blockers. The mean length of stay in ICU/CCU was 3.15 (18.65) days (median, 2 days), depending on age, a coronariography having previously been performed, the Killip classification, having required coronariography and PCI or echocardiography or mechanical ventilation, and presenting complications such as angina that is difficult to control, arrhythmia, right ventricular failure or death. CONCLUSIONS: The factors are associated to mortality were; greater age, diabetes, Killip classification, arrhythmia, cardiogenic shock and the need for CPR, whereas smoking is associated to a lower mortality rate. The patients on whom PCI was performed represent a less severe population. Management has changed over the years, with an increase in PCI. A prolonged mean length of stay is associated to the appearance of arrhythmia, right or left heart failure, angina that is difficult to control, age and PCI.