An efficient acoustic events detection system EAR-TUKE is presented in this paper. The system is capable of processing continuous input audio stream in order to detect potentially dangerous acoustic events, specifically gunshots or breaking glass. The system is programmed entirely in C++ language (core math. functions in C) and was designed to be self sufficient without requiring additional dependencies. In the design and development process the main focus was put on easy support of new acoustic events detection, low memory profile, low computational requirements to operate on devices with low resources, and on long-term operation and continuous input stream monitoring without any maintenance. In order to satisfy these requirements on the system, EAR-TUKE is based on a custom approach to detection and classification of acoustic events. The system is using acoustic models of events based on Hidden Markov Models (HMMs) and a modified Viterbi decoding process with an additional module to allow continuous monitoring. These features in combination with Weighted Finite-State Transducers (WFSTs) for the search network representation fulfill the easy extensibility requirement. Extraction algorithms for Mel-Frequency Cepstral Coefficients (MFCC), Frequency Bank Coefficients (FBANK) and Mel-Spectral Coefficients (MELSPEC) are also included in the preprocessing part. The system contains Cepstral Mean Normalization (CMN) and our proposed removal of basic coefficients from feature vectors to increase robustness. This paper also presents the development process and results evaluating the final design of the system.

This paper describes an extension of an intelligent acoustic event detection system, which is able to recognize sounds of dangerous events such as breaking glass or gunshot sounds in urban environment from commonly used noise monitoring stations. We propose to extend the system the way that it would not only detect the gunshots, but it would identify a suspects gun/pistol type as well. Such extension could help the investigation process and the suspect identification. The proposed extension provides a new functionality of the gun type recognition (classification) based on audio recordings captured. This research topic is discussed in other research papers marginally. Different kinds of features were extracted for this challenging task and feature vectors were reduced by using mutual information based feature selection algorithms. The proposed system uses two phase selection process, HMM (Hidden Markov Model) classification and Viterbi based decoding algorithm. The presented approach reached promising results in the experiments (higher than 80% of ACC and TPR).

This paper describes the development of the speech audiometry application for pediatric patients in Slovak language and experiences obtained during testing with healthy children, hearing-impaired children, and elderly persons. The first motivation behind the presented work was to reduce the stress and fear of the children, who must undergo postoperative audiometry, but over time, we changed our direction to the simple game-like mobile application for the detection of possible hearing problems of children in the home environment. Conditioned play audiometry principles were adopted to create a speech audiometry application, where children help the virtual robot Thomas assign words to pictures; this can be described as a speech recognition test. Several game scenarios together with the setting condition issues were created, tested, and discussed. First experiences show a positive influence on the children’s mood and motivation.