Novelty detection in the machine learning context refers to identifying unknown/novel data, i.e., data which vary greatly from the ones that the system was trained with. This paper explores this technique as applied to acoustic surveillance of abnormal situations. The ultimate goal of the system is to help an authorized person towards taking the appropriate actions for preventing life/property loss. A wide variety of acoustic parameters is employed towards forming a multidomain feature vector, which captures diverse characteristics of the audio signals. Subsequently the feature coefficients are fed to three probabilistic novelty detection methodologies. Their performance is computed using two measures which take into account misdetections and false alarms. Out dataset was recorded under real-world conditions including three different locations where various types of normal and abnormal sound events were captured. A smart-home environment, an open public space, and an office corridor were used. The results indicate that probabilistic novelty detection can provide an accurate analysis of the audio scene to identify abnormal events.

The present study presents a practical methodology for automatic space monitoring based solely on the perceived acoustic information. We consider the case where atypical situations such as screams, explosions and gunshots take place in a metro station environment. Our approach is based on a two stage recognition schema, each one exploiting HMMs for approximating the density function of the corresponding sound class. The main objective is to detect abnormal events that take place in a noisy environment. A thorough evaluation procedure is carried out under different SNR conditions and we report high detection rates with respect to false alarm and miss probabilities rates.

This paper presents an anomaly-based methodology for reliable detection of integrity attacks in cyber-physical critical infrastructures. Such malicious events compromise the smooth operation of the infrastructure while the attacker is able to exploit the respective resources according to his/her purposes. Even though the operator may not understand the attack, since the overall system appears to remain in a steady state, the consequences may be of catastrophic nature with a huge negative impact. Here, we apply a computational intelligent technique which incorporates the merits of two of the heterogeneous modeling approaches (linear time-invariant and neural networks), while considering both temporal and functional dependencies existing among the elements of an infrastructure. The experimental platform includes a power grid simulator of the IEEE 30 bus model and a cyber network emulator. Subsequently, we implemented a wide range of integrity attacks (replay, ramp, pulse, scaling, and random) with different intensity levels. A thorough evaluation procedure is carried out while the results demonstrate the ability of the proposed method to produce a desired result in terms of false positive rate, false negative rate, and detection delay.

During recent years, the field of emotional content analysis of speech signals has been gaining a lot of attention and several frameworks have been constructed by different researchers for recognition of human emotions in spoken utterances. This paper describes a series of exhaustive experiments which demonstrate the feasibility of recognizing human emotional states via integrating low level descriptors. Our aim is to investigate three different methodologies for integrating subsequent feature values. More specifically, we used the following methods: 1) short-term statistics, 2) spectral moments, and 3) autoregressive models. Additionally, we employed a newly introduced group of parameters which is based on the wavelet decomposition. These are compared with a baseline set comprised of descriptors which are usually used for the specific task. Subsequently, we experimented on fusing these sets on the feature and log-likelihood levels. The classification step is based on hidden Markov models, while several algorithms which can handle redundant information were used during fusion. We report results on the well-known and freely available database BERLIN using data of six emotional states. Our experiments show the importance of including information which is captured by the set based on multiresolution analysis and the efficacy of merging subsequent feature values.

Detecting and isolating faults in cyber-physical systems (CPSs), e.g., critical infrastructures, smart buildings/cities, and the internet-of-things, are tasks that do generally scale badly with the CPS size. This work introduces a model-free fault detection and diagnosis system (FDDS) designed, having in mind scalability issues, so as to be able to detect and isolate faults in CPSs characterised by a large number of sensors. Following the model-free approach, the proposed FDDS learns the nominal fault-free conditions of the large-scale CPS autonomously by exploiting the temporal and spatial relationships existing among sensor data. The novelties in this paper reside in 1) a clustering method proposed to partition the large-scale CPS into groups of highly correlated sensors in order to grant scalability of the proposed FDDS, and 2) the design of model- and fault-free mechanisms to detect and isolate multiple sensor faults, and disambiguate between sensor faults and time variance of the physical phenomenon the cyber layer of CPS inspects.