Abstract. Monitoring the road pavement is a challenging task. Authorities spend time and finances to monitor the state and quality of the road pavement. This paper investigate road surface monitoring with smartphones equipped with GPS and inertial sensors: accelerometer and gyroscope. In this study we describe the conducted experiments with data from the time domain, frequency domain and wavelet transformation, and a method to reduce the effects of speed, slopes and drifts from sensor signals. A new audiovisual data labelling technique is proposed. Our system named RoADS, implements wavelet decomposition analysis for signal processing of inertial sensor signals and Support Vector Machine (SVM) for anomaly detection and classification. Using these methods we are able to build a real time multi class road anomaly detector. We obtained a consistent accuracy of ≈90% on detecting severe anomalies regardless of vehicle type and road location. Local road authorities and communities can benefit from this system to evaluate the state of their road network pavement in real time.

Ground transport infrastructures require in-situ monitoring to evaluate their condition and deterioration and to design appropriate preventive maintenance strategies. Current monitoring practices provide accurate and detailed spatial measurements but often lack the required temporal resolution. This is because the large scale of these infrastructures and the expensive equipments required for monitoring activities do not allow running very frequent measurement campaigns. In this paper, we present RoVi, a novel smartphone-based framework for continuous monitoring of a number of health and condition indicators for variety of ground infrastructures and assets. These indicators include railroad track geometry features such as Cant, Twist, Curvature, and Alignment for different segment lengths as well as road and bike path roughness index (i.e., an equivalent to the International Roughness Index, the so called IRI).RoVi uses an optimized processing algorithm technique on data acquired by smartphones' inertial sensors and relies on sensing, processing power, and networking capabilities of smartphones carried by car/bike drivers and train passengers to provide real time space-time information for fine-grained monitoring of infrastructures. It utilizes the crowd sensing concept to fill in the gap between current sparse consecutive inspections. RoVi provides a reliable and accurate analytic tool for engineers and maintenance planners by offering them features and indicators they require for asset management and maintenance planning. We extract these features and indicators from noisy smartphone data utilizing adaptive signal processing techniques followed by feature calculations and geo-location visualization. Our fast data aggregation algorithm based on Delaunay triangulation updates profiles with new measurements arriving in real time from smartphones. By doing so, it tackles the notorious problem of smartphone GPS accuracy.Performance evaluation of our framework has been performed on measurements collected by smartphones and compared with the ground truth measurements collected by the highend measurement vehicles (i.e., ARAN for roads and UMF120 measurement train for railroads).

This paper introduces a method to detect road anomalies by analyzing driver behaviours. The analysis is based on the data and the features extracted from smartphone inertial sensors to calculate the angle of swerving and also based on distinctive states of a driver behaviour event. A novel approach is introduced to deal with the gyroscope drift, reducing the average angle estimation error for curves up to 2 • and the overall average angle error up to 5 • . Using a simple machine learning approach and a clustering algorithm, the method can detect 70% of the swerves and 95% of the turns on the road.

This paper presents a comparative study on dierent feature extraction and machine learning techniques for indoor environmental sound classication. Compared to outdoor environmental sound classication systems, indoor systems need to pay special attention to power consumption and privacy. We consider feature calculation complexity, classication accuracy and privacy as evaluation metrics. To ensure privacy, we strip voice bands from sound input to make human conversations unrecognizable. With 5 classes of 2500 indoor audio events as input, our experimental results show that using SVM model with LPCC feature, 78% classication accuracy can be reached. Furthermore, the performance is improved to more than 85% by combining several simple features and dropping unreliable predictions, which only slightly increase the complexity. CCS CONCEPTS • Computing methodologies → Feature selection; • Applied computing → Sound and music computing; • Human-centered computing → Ubiquitous and mobile computing theory, concepts and paradigms.

People counting techniques have been widely researched recently and many different types of sensors can be used in this context. In this paper, we propose a system based on a deep-learning model able to identify the number of people in the crowded scenarios through the speech sound. In a nutshell the system relies on two components: counting concurrent speakers in overlapping talking sound directly and clustering singlespeaker sound by speaker-identity over time. Compared to previously proposed speaker-counting systems models that only cluster single-speaker sound, this system is more accurate and less vulnerable to the overlapping sound in the crowded environment. In addition, counting speakers in overlapping sound also gives the minimal number of speakers so that it also improves the counting accuracy in a quiet environment.Our methodology is inspired by the newly proposed SincNet deep neural network framework which proves to be outstanding and highly efficient in sound processing with raw signals. By transferring the bottleneck layer of SincNet model as features fed to our speaker clustering model we reached a noticeably better performance than previous models who rely on the use MFCC and other engineered features.