Accurate source localization is an important problem in many research areas as well as practical applications in wireless communications and acoustic signal processing. This paper presents a passive three-dimensional sound source localization (SSL) method that employs a geometric configuration of three soundfield microphones. Two methods for estimating the angle of arrival (AOA) and time difference of arrival (TDOA) are proposed based on Ambisonics A and B format signals. The closed-form solution for sound source location estimation based on two TDOAs and three AOAs is derived. The proposed method is evaluated by simulations and physical experiments in our anechoic chamber. Simulations demonstrate that the estimation method can theoretically obtain Cramér-Rao lower bound for a small Gaussian noise present in AOA and TDOA observations. Investigation on the uncertainty of TDOA and AOA measurements depending on the length of measurement interval is also conducted. Experimental results in terms of RMSE indicate that the proposed solution can be used to accurately find a 3D position of the sound source in free-field environment. Performance evaluation regarding the number of estimation steps shows that higher accuracy can be achieved by longer observations of stationary sound source.

This article investigates the use of an audio augmented reality (AAR) system to recreate the soundscape of a medieval archaeological site. The aim of our work was to explore whether it is possible to enhance a tourist's archaeological experience, which is often derived from only scarce remains. We developed a smartphone-based AAR system, which uses location and orientation sensors to synthesize the soundscape of a site and plays it to the user via headphones. We recreated the ancient soundscape of a medieval archaeological site in Croatia and tested it in situ on two groups of participants using the soundwalk method. One test group performed the soundwalk while listening to the recreated soundscape using the AAR system, while the second control group did not use the AAR equipment. We measured the experiences of the participants using two methods: the standard soundwalk questionnaire and affective computing equipment for detecting the emotional state of participants. The results of both test methods show that participants who were listening to the ancient soundscape using our AAR system experienced higher arousal than those visiting the site without AAR.

Accurate speech recognition can provide a natural interface for human–computer interaction. Recognition rates of the modern speech recognition systems are highly dependent on background noise levels and a choice of acoustic feature extraction method can have a significant impact on system performance. This paper presents a robust speech recognition system based on a front-end motivated by human cochlear processing of audio signals. In the proposed front-end, cochlear behavior is first emulated by the filtering operations of the gammatone filterbank and subsequently by the Inner Hair cell (IHC) processing stage. Experimental results using a continuous density Hidden Markov Model (HMM) recognizer with the proposed Gammatone Hair Cell (GHC) coefficients are lower for clean speech conditions, but demonstrate significant improvement in performance in noisy conditions compared to standard Mel-Frequency Cepstral Coefficients (MFCC) baseline.

This paper presents the beam tracing with refraction method, developed to examine the possibility of creating the beam tracing simulation of sound propagation in environments with piecewise non- homogenous media. The beam tracing with refraction method (BTR) is developed as an adaptive beam tracing method that simulates not only the reflection but also the refraction of sound. The scattering and the diffraction of sound are not simulated. The BTR employs 2D and 3D topology in order to efficiently simulate scenes containing non-convex media. After the beam tracing is done all beams are stored in a beam tree and kept in the computer memory. The level of sound intensity at the beginning of each beam is also memorized. This beam data structure enables fast recalculation of results for stationary source and geometry. The BTR was compared with two commercial ray tracing simulations, to check the speed of BTR algorithms. This comparison demonstrated that the BTR has a performance similar to state-of- the-art room-acoustics simulations. To check the ability to simulate refraction, the BTR was compared with a commercial Finite Elements Method (FEM) simulation. In this comparison the BTR simulated the focusing of the ultrasound with an acoustic lens, with good accuracy and excellent performance.

This paper presents the usage of artificial neural networks (NNs) in bicycle route planning. This research aimed to check the possibility of NNs to transfer human expertise in bicycle route design by training the NN on an already established set of bicycle routes and then using the trained NN to design the routes on the novel area. We created two NNs capable of choosing the best route among the given road network by training them on two different areas. The bicycle routes produced by NNs were the same at best and had 75% overlap at the worst compared to those produced by human experts. Furthermore, the mean square error for all of our NN models varied from 0.015 and 0.081. We compared this new approach to the traditional multicriteria GIS (geographic information system) analysis (MA) that requires the human expert to define the bicycle route selection criteria. The benefit of using NN over the MA was that the NN directly transfers the human expertise to a model. In contrast, the MA needs the expert to select multiple criteria and adjust their weights carefully.