Alessandro Delfino

Gender-Driven Emotion Recognition Through Speech Signals For Ambient Intelligence Applications

This paper proposes a system that allows recognizing a persons emotional state starting from audio signal registrations. The provided solution is aimed at improving the interaction among humans and computers, thus allowing effective human-computer intelligent interaction. The system is able to recognize six emotions(anger, boredom, disgust, fear, happiness, and sadness) and the neutral state. This set of emotional states is widely used for emotion recognition purposes. It also distinguishes a single emotion versus all the other possible ones, as proven in the proposed numerical results. The system is composed of two subsystems: 1) gender recognition(GR) and 2) emotion recognition(ER). The experimental analysis shows the performance in terms of accuracy of the proposed ER system. The results highlight that the a priori knowledge of the speakers gender allows a performance increase. The obtained results show also that the features selection adoption assures a satisfying recognition rate and allows reducing the employed features. Future developments of the proposed solution may include the implementation of this system over mobile devices such as smartphones.

A Television Channel Real-Time Detector using Smartphones

Recently, people have been interested in sharing what they are watching on TV, allowing the development of Social TV Applications often based on mobile devices. In this context, this paper proposes IRTR (Improved Real-Time TV-channel Recognition): a new method aimed at recognizing in real time (live) what people are watching on TV without any active user interaction. IRTR uses the audio signal of the TV program recorded by smartphones and is performed through two steps: i) fingerprint extraction and ii) TV channel real-time identification. Step i) is based on the computation of the Audio Fingerprint (AF). The AF computation has been taken from the literature and has been improved in terms of power consumption and computation speed to make the smartphone implementation feasible by using an ad hoc cost function aimed at selecting the best set of AF parameters. Step ii) is aimed at deciding the TV channel the user is watching. This step is performed using a likelihood estimation algorithm proposed in this paper. The consumed power, computation and response time, and correct decision rate of IRTR, evaluated through experimental measures, show very satisfying results such as a correct decision rate of about 95%, about 2s of computation time, and above 90% power saving with respect to the literature.

Enabling IoT for In-Home Rehabilitation: Accelerometer Signals Classification Methods for Activity and Movement Recognition

Rehabilitation and elderly monitoring for active aging can benefit from Internet of Things (IoT) capabilities in particular for in-home treatments. In this paper, we consider two functions useful for such treatments: 1) activity recognition (AR) and 2) movement recognition (MR). The former is aimed at detecting if a patient is idle, still, walking, running, going up/down the stairs, or cycling; the latter individuates specific movements often required for physical rehabilitation, such as arm circles, arm presses, arm twist, curls, seaweed, and shoulder rolls. Smartphones are the reference platforms being equipped with an accelerometer sensor and elements of the IoT. The work surveys and compares accelerometer signals classification methods to enable IoT for the aforementioned functions. The considered methods are support vector machines (SVMs), decision trees, and dynamic time warping. A comparison of the methods has been proposed to highlight their performance: all the techniques have good recognition accuracies and, among them, the SVM-based approaches show an accuracy above 90% in the case of AR and above 99% in the case of MR.

A simple ultrasonic Indoor/Outdoor detector for mobile devices

Context information is fundamental for mobile application. A system able to detect Indoor/Outdoor state switching can give useful information to upper-level applications permitting to improve their performance or reduce the computational load and consequently the lifetime of the smartphone. A localization application may exploit the Indoor/Outdoor information to smartly decide if using GPS (that performs well outdoors but poorly indoors) or other localization methods. In this paper we present an ultrasonic-signal-based Indoor/Outdoor detector for smartphones. The phone plays an ultrasonic ping by using its in-built speakers and records the echoes by using its microphone, therefore, no specific hardware is required to be added to the smartphone. The proposed detector shows good performance in terms of accuracy and latency.

Fast audio fingerprint comparison for real-time TV-channel recognition applications

This paper considers IRTR (Improved Real-Time TV-channel Recognition), a new method aimed at recognizing in real-time (live) what people is watching on TV, similarly to the action performed by Audience investigations, but without any TV user active interaction. IRTR uses only the audio signal of the TV program recorded through smartphones and is independent of the specific smartphone technology. It is performed through two main steps: i) fingerprint extraction and ii) TV channel real-time identification. This paper proposes a likelihood estimation-based algorithm aimed at performing the second step. The computational time of the proposed approach has been evaluated through real measures and shows really satisfying results.

Enabling smartphone-centric platforms for in-home rehabilitation: A comparison among movement recognition approaches

In-home physical therapy is one of the best options for many individuals and families thanks to its convenience and because it makes possible to receive professional care in the comfort of your own home. To enable this therapeutic approach, this paper proposes the employment of a smartphone-centric platform for in-home rehabilitation. The platform helps physicians to monitor the patients remotely so avoiding hospitalization therapies that can be stressful. In more detail, the work is focused on the Movement Recognition (MR) functionality of the aforementioned platform. It compares algorithms, which process the signal provided by the embedded accelerometer sensor of the smartphone, able to recognize if a patient had performed the movements requested by the physicians. The provided performance comparison of different MR techniques shows that Support Vector Machine-based approaches have very good accuracy (up to 99.3%), thus making the in-home physical therapy reliable.

Poster: Detecting if a Smartphone is Indoors or Outdoors with Ultrasounds

Context information is fundamental for mobile application. A system able to detect if a smartphone is indoors or outdoors can give useful information to upper-level applications, permitting to improve their performance or reduce the computational load and consequently the lifetime of the smartphone battery. For example, GPS provides an accurate location reference in the outdoor environment while it performs poorly inside buildings. The proposed Indoor/Outdoor (IO) detector can provide a useful essential information to a localization application that can check whether the user is outdoors before turning on the GPS interface and decide not to turn it on and use other localization methods if the user is detected indoors. In mobile data services, mobile phones normally observe more WiFi access points (APs) with strong signals inside buildings, whereas it is unlikely to have good WiFi connections in outdoor environments. Therefore, knowing whether the smartphone is indoors or outdoors can help to make smarter decisions regarding whether to perform or not AP scans. Although it is clear that various applications may benefit from accurate and prompt indoor/outdoor information, the research work on indoor/outdoor detection of mobile devices is still lacking. There are mainly two techniques to perform such detection. One is to use GPS and its signal quality as a cue to infer if the user is indoor. This technique is proven to be highly power consuming. Another technique is to leverage the sensors which the smartphone is equipped with. IO detection can be done by exploiting lightweight sensors such as the light sensor, the radio interface and the magnetism sensor [1]. The proposed IO detector is an active system. The phone periodically emits an ultrasonic ping using its in-built speakers and continuously listens for the echoes through its microphone. It is impossible to identify the direction of the echoes being the microphone (as well as the phone speakers) non-directional. The idea is that indoors the number and the intensity of the echoes should be higher than outdoors due to the higher number of obstacles. Translating such an idea into a practical Indoor/Outdoor detector means finding those features that model such behaviour. The proposed

Hybrid Simulated-Emulated Platform for Heterogeneous Access Networks Performance Investigations

The availability of different access technologies enables the creation of heterogeneous networks supporting users mobility and assuring several different services. Meanwhile these networks require complex control techniques to assure Quality of Service (QoS) to users. Before implementing such networks, a deep performance analysis, through the use of network simulators or real models, is necessary. In particular the first ones (e.g. Network Simulator 3 - ns-3 among the others) are quite simple and easy to manage and configure, while the second ones assure the handling of real traffic flows. The main contribution of this paper is the description of an hybrid simulated and emulated network evaluation platform, developed by the authors. The platform purpose is to execute a performance analysis of different wireless networks such as Long Term Evolution (LTE) and Wi-Fi, connected to a core network implementing the Differentiated Service (DiffServ) protocol. The paper contains also the results of preliminary validation tests.

A Heuristic Attack Method to PRH-Based Audio Copy Detectors

Often copyrighted multimedia files are uploaded and shared online. To avoid the unregulated spread of such material many copy detectors have been developed in order to deny the possibility to upload, and consequently make available, copies of copyrighted contents. A widely referenced fingerprint method for content-based audio identification is the Philips Robust Hash (PRH) . This paper introduces a simple but effective attack technique capable to defeat a PRH fingerprint-based audio copy detector without significantly affecting the signal quality. It is a heuristic method that adds a suitable distortion to the original audio signal, so that the modified signal is not detected as a copy of the original one but is perceptively very similar to it. The quality of the modified signal has been evaluated in terms of a distortion measure based on a mathematical model of the human auditory system and of the Peak Signal-to-Noise Ratio (PSNR). The attack method has shown a promising success rate.

Hybrid simulated-emulated platform for heterogeneous access networks performance investigations

The availability of different access technologies enables the creation of heterogeneous networks supporting users mobility and assuring several different services. Meanwhile these networks require complex control techniques to assure Quality of Service (QoS) to users. Before implementing such networks, a deep performance analysis, through the use of network simulators or real models, is necessary. In particular the first ones (e.g. Network Simulator 3 - ns-3 among the others) are quite simple and easy to manage and configure, while the second ones assure the handling of real traffic flows. The main contribution of this paper is the description of an hybrid simulated and emulated network evaluation platform, developed by the authors. The platform purpose is to execute a performance analysis of different wireless networks such as Long Term Evolution (LTE) and Wi-Fi, connected to a core network implementing the Differentiated Service (DiffServ) protocol. The paper contains also the results of preliminary validation tests.