Human emotions detection considering speech signals is a field that has attracted the attention of the research community since the last years. Several situations where the human integrity and security is at risk have been addressed; particularly the analysis of speech in emergency calls or in call-centers, are an interesting scenario. This project aimed to develop a methodology to classify different types of emotions such as anger, anxiety, disgust, and desperation, in scenarios where the speech signal is contaminated with noise or is coded by telephone channels.
A significant amount of energy is wasted in industrial operations due to several factors such as electricity theft, fraud, billing errors, and fault devices, among others. It is important to monitor the behavior of the electricity consumption in order to improve energy usage efficiency. Smart meters have been developed in order to collect information about the electricity consumption behaviors and lifestyles of the consumers. One of the most important applications to monitor energy consumption corresponds to anomaly detection. The main challenge in this application is the lack of fully labeled datasets with annotated information about the presence of anomalies in the time-series. The prpject proposes an unsupervised approach where a deep learning strategy that combines autoencoders and recurrent neural networks is used to detect anomalies in time-series of energy consumption.
Asynchronous Non-Intrusive Multi-Modal Analysis of Bio-Signals for the Automatic evaluation of the Neurological State of People With Parkinson's Disease
Parkinson’s disease is a neuro-degenerative disorder characterized by different motor symptoms, including several gait impairParkinson’s disease (PD) is a neuro-degenerative disorder that produces motor and non-motor impairments. Non-motor …
The feature vectors of a data set encode information about relations between speaker groups, clusters and outliers. Based on the assumption that these relations are conserved within the spatial properties of feature vectors, we introduce similarity …
Background and objectives: Parkinson’s disease is a neurological disorder that affects the motor system producing lack of coordination, resting tremor, and rigidity. Impairments in handwriting are among the main symptoms of the disease. Handwriting …
Speech deficits are common symptoms amongParkinson’s Disease (PD) patients. The automatic assessment of speech signals is promising for the evaluation of the neurological state and the speech quality of the patients. Recently, progress has been made …