Experiments on the DCASE Challenge 2016: Acoustic Scene Classification and Sound Event Detection in Real Life Recording

In this paper we present our work on Task 1 Acoustic Scene Classi- fication and Task 3 Sound Event Detection in Real Life Recordings. Among our experiments we have low-level and high-level features, classifier optimization and other heuristics specific to each task. Our performance for both tasks improved the baseline from DCASE: for Task 1 we achieved an overall accuracy of 78.9% compared to the baseline of 72.6% and for Task 3 we achieved a Segment-Based Error Rate of 0.76 compared to the baseline of 0.91

Experiments on the DCASE Challenge 2016: Acoustic scene classification and sound event detection in real life recording

International audienceIn this paper we present our work on Task 1 Acoustic Scene Classification and Task 3 Sound Event Detection in Real Life Recordings. Among our experiments we have low-level and high-level features, classifier optimization and other heuristics specific to each task. Our performance for both tasks improved the baseline from DCASE: for Task 1 we achieved an overall accuracy of 78.9% compared to the baseline of 72.6% and for Task 3 we achieved a Segment-Based Error Rate of 0.48 compared to the baseline of 0.91

Classificação de sons urbanos usando motifs e MFCC

A classificação automática de sons urbanos é importante para o monitoramento ambiental. Este trabalho apresenta uma nova metodologia para classificar sons urbanos, que se baseia na descoberta de padrões frequentes (motifs) nos sinais sonoros e utiliza-los como atributos para a classificação. Para extrair os motifs é utilizado um método de descoberta multi-resolução baseada em SAX. Para a classificação são usadas árvores de decisão e SVMs. Esta nova metodologia é comparada com outra bastante utilizada baseada em MFCC. Para a realização de experiências foi utilizado o dataset UrbanSound disponível publicamente. Realizadas as experiências, foi possível concluir que os atributos motif são melhores que os MFCC a discriminar sons com timbres semelhantes e que os melhores resultados são conseguidos com ambos os tipos de atributos combinados. Neste trabalho foi também desenvolvida uma aplicação móvel para Android que permite utilizar os métodos de classificação desenvolvidos num contexto de vida real e expandir o dataset.The automatic classification of urban sounds is important for environmental monitoring. This work presents a new method to classify urban sounds based on frequent patterns (motifs) in the audio signals and using them as classification attributes. To extract the motifs, a multiresolution discovery based on SAX is used. For the classification itself, decision trees and SVMs are used. This new method is compared with another largely used based on MFCCs. For the experiments, the publicly available UrbanSound dataset was used. After the experiments, it was concluded that motif attributes are better to discriminate sounds with similar timbre and better results are achieved with both attribute types combined. In this work was also developed a mobile application for Android which allows the use of the developed classifications methods in a real life context and to expand the dataset

Conception et implémentation d'un réseau sans-fil pour la surveillance continue des signes vitaux

Les dépenses de santé augmentent continuellement année après année et prennent une grande partie du budget d’un pays. Pendant les soins médicaux, les signes vitaux, tels que le rythme cardiaque et la respiration, sont des paramètres clés qui sont surveillés en permanence. La toux est un indicateur important de plusieurs problèmes comme la maladie pulmonaire obstructive chronique (MPOC), et c’est aussi la principale raison pour laquelle les patients consultent un médecin. En fait, c’est un mécanisme de défense pulmonaire des voies respiratoires qui permet l’expulsion de substances indésirables et irritantes. Les capteurs de corps sans fil sont de plus en plus utilisés par les cliniciens et les chercheurs, dans un large éventail d’applications telles que le sport, l’ingénierie spatiale et la médecine. La surveillance des signes vitaux en temps réel peut considérablement augmenter la précision du diagnostic et peut permettre des méthodes de guérison automatiques, par exemple, la détection et l’arrêt des crises d’épilepsie ou de narcolepsie. Les paramètres respiratoires sont essentiels en oxygénothérapie, en milieu hospitalier et en surveillance ambulatoire, tandis que l’évaluation de la sévérité de la toux est essentielle pour traiter plusieurs maladies, comme la bronchopneumopathie chronique obstructive (BPCO). Dans cette thèse, un système de surveillance respiratoire sans fil de faible puissance avec détection de la toux est présenté. Ce système utilise des capteurs multimodaux, portables et sans-fils, conçus à l’aide de composants conventionnels disponibles dans le commerce. Ces capteurs portables utilisent une unité de mesure inertielle à 9 axes de faible puissance pour mesurer la fréquence respiratoire, et un microphone MEMS pour effectuer la détection de la toux. L’architecture de chaque capteur sans fil est présentée. De plus, les résultats montrent que le capteur à petite taille de 26,67 x 65,53 mm² consomme environ 12 à 16,2 mA et peut durer au moins 6 heures avec une batterie lithium-ion miniature de 100 mA. L’unité d’acquisition, l’unité de communication sans fil et les algorithmes de traitement de données sont décrits. Les performances du réseau de capteurs sont présentées pour des tests expérimentaux en comparant avec la pléthysmographie d’inductance respiratoire.Health care expenses are continuously increasing year after year and taking a large part of a country’s budget. During medical care, vital signs, such as heart and breathing rates, are key parameters that are continuously monitored. Coughing is a prominent indicator of several problems such as COPD, and it is also the main reason for why patients seek medical advice. In fact, it is a pulmonary defense mechanism of the respiratory tract that allows the expulsion of undesirable and irritating substances. Wireless body sensors are increasingly used by clinicians and researchers, in a wide range of applications such as sports, space engineering and medicine. Monitoring vital signs in real time can dramatically increase diagnosis accuracy and enable automatic curing procedures, e.g. detect and stop epilepsy or narcolepsy seizures. Breathing parameters are critical in oxygen therapy, hospital and ambulatory monitoring, while the assessment of cough severity is essential when dealing with several diseases, such as chronic obstructive pulmonary disease (COPD). In this thesis, a low-power wireless respiratory monitoring system with cough detection is proposed to measure the breathing rate and the frequency of coughing. This system uses wearable wireless multimodal patch sensors, designed using off the shelf components. These wearable sensors use a low-power 9-axis inertial measurement unit to measure the respiratory frequency, and a MEMs microphone to perform cough detection. The architecture of each wireless patch-sensor is presented. In fact, the results show that the small 26.67 x 65.53 mm² patch-sensor consumes around 12 to 16.2 mA, and can last at least 6 hours with a miniature 100 mA lithium ion battery. The acquisition unit, the wireless communication unit and the data processing algorithms are described. The proposed network performance is presented for experimental tests with a freely behaving user in parallel with the gold standard respiratory inductance plethysmograph