Józef Kotus

Detection and localization of selected acoustic events in acoustic field for smart surveillance applications

A method for automatic determination of position of chosen sound events such as speech signals and impulse sounds in 3-dimensional space is presented. The events are localized in the presence of sound reflections employing acoustic vector sensors. Human voice and impulsive sounds are detected using adaptive detectors based on modified peak-valley difference (PVD) parameter and sound pressure level. Localization based on signals from the multichannel acoustic vector probe is performed upon the detection. The described algorithms can be employed in surveillance systems to monitor behavior of public events participants. The results can be used to detect sound source position in real time or to calculate the spatial distribution of sound energy in the environment. Moreover, the spatial filtration can be performed to separate sounds arriving from a chosen direction.

Detection and Localization of Selected Acoustic Events in 3D Acoustic Field for Smart Surveillance Applications

A method for automatic determination of position of chosen sound events such as speech signals and impulse sounds in 3-dimensional space is presented. The events are localized in the presence of sound reflections employing acoustic vector sensors. Human voice and impulsive sounds are detected using adaptive detectors based on modified peak-valley difference (PVD) parameter and sound pressure level. Localization based on signals from the multichannel acoustic vector probe is performed upon the detection. The described algorithms can be useful in a surveillance systems to monitor the behavior of participants of public events. The results can be used to detect the position of sound source in real time or to calculate the spatial distribution of sounds in the environment. Moreover, spatial filtration can be performed to separate the sounds coming from the chosen direction.

Behavior Analysis and Dynamic Crowd Management in Video Surveillance System

A concept and practical implementation of a crowd management system which acquires input data by the set of monitoring cameras is presented. Two leading threads are considered. First concerns the crowd behavior analysis. Second thread focuses on detection of a hold-ups in the doorway. The optical flow combined with soft computing methods (neural network) is employed to evaluate the type of crowd behavior, and fuzzy logic aids detection of the hold-ups. The experiments with the behavior classification algorithm were performed employing prepared repository of typical and untypical behavior recordings. The effectiveness of the analysis was assessed by comparing algorithmic processing results to a set of prepared reference data, which provides a description of behavior type occurring in each video frame. Application of parallel image processing and influence of parallelization on achieved performance is explained. Apart from the crowd management the behavior analysis may be used in automatic surveillance system deployed in a city area.

Multiple sound sources localization in free field using acoustic vector sensor

Method and preliminary results of multiple sound sources localization in free field using the acoustic vector sensor were presented in this study. Direction of arrival (DOA) for considered source was determined based on sound intensity method supported by Fourier analysis. Obtained spectrum components for considered signal allowed to determine the DOA value for the particular frequency independently. The accuracy of the developed and practically implemented algorithm was evaluated on the basis of laboratory tests. Both synthetic acoustic signals (pure tones and noises) and real sounds were used during the measurements. Real signals had the same or different energy distribution both on time and frequency domain. The setup of the experiment and obtained results were described in details in the text. Taking the obtained results into consideration is important to emphasize that the localization of the multiple sound sources using single acoustic vector sensor is possible. The localization accuracy was the best for signals which spectral energy distribution was different.

Multiple sound sources localization in real time using acoustic vector sensor

Method and preliminary results of multiple sound sources localization in real time using the acoustic vector sensor were presented in this study. Direction of arrival (DOA) for considered source was determined based on sound intensity method supported by Fourier analysis. Obtained spectrum components for considered signal allowed to determine the DOA value for the particular frequency independently. The accuracy of the developed and practically implemented algorithm was evaluated on the basis of laboratory tests. Both synthetic acoustic signals (pure tones and noises) and real sounds were used during the measurements. Real signals had the same or different spectral energy distribution both on time and frequency domain. The setup of the experiment and obtained results were described in details in the text. Taking obtained results into consideration is important to emphasize that the localization of the multiple sound sources using single acoustic vector sensor is possible. The localization accuracy was the best for signals which spectral energy distribution was different.

Problems of railway noise-a case study.

Under Directive 2002/49/EC relating to the assessment and management of environmental noise, all European countries are obliged to model their environmental noise levels in heavily populated areas. Some countries have their own national method, to predict noise but most have not created one yet. The recommendation for countries that do not have their own model is to use an interim method. The Dutch SRM II scheme is suggested for railways. In addition to the Dutch model, this paper describes and discusses 3 other national methods. Moreover, discrepancies between the HARMONOISE and IMAGINE projects are analysed. The results of rail traffic noise measurements are compared with national methods.

Gaze-tracking and Acoustic Vector Sensors Technologies for PTZ Camera Steering and Acoustic Event Detection

An innovative application of gaze-tracking and acoustic vector sensors (AVS) technologies for guidance of moving pan-tilt-zoom (PTZ) monitoring camera is presented. Gaze-tracking is used to steer and to zoom the camera to the gaze focus area. Additionally, it is combined with audio processing in two scenarios. First is called “audio slave”: directional acoustic monitoring is adjusted automatically to the camera direction. Second is called “audio master”: automatic detection of sound events directions is performed to take priority over user control and steer the camera towards sound source. An approach to gaze tracking is presented, utilizing new algorithmic methods for both image processing and PTZ camera steering. Then application of AVS for directional filtering of sound, and for detection of acoustic events direction is discussed. The implemented application is described, and user experience is reported. Finally, future work is discussed.

Online urban acoustic noise monitoring system

Concepts and implementation of the Online Urban Noise Monitoring System are presented. Principles of proposed solution used for dynamic acoustical maps creating are discussed. The architecture of the system and the data acquisition scheme are described. The concept of noise mapping, based on noise source model and propagation simulations, was developed and employed in the system. Dynamic estimation of noise source parameters utilized in the system is introduced. The details of implementation of noise map computation and visualization are presented. Advances introduced by the developed solution in the continuous noise monitoring and acoustic maps creation is in focus. The results of measurements and simulations performed by the system prototype are depicted. Noise measurements results gathered by system and created acoustic maps are compared with some other solutions in order to investigate accuracy

An audio-visual corpus for multimodal automatic speech recognition

A review of available audio-visual speech corpora and a description of a new multimodal corpus of English speech recordings is provided. The new corpus containing 31 hours of recordings was created specifically to assist audio-visual speech recognition systems (AVSR) development. The database related to the corpus includes high-resolution, high-framerate stereoscopic video streams from RGB cameras, depth imaging stream utilizing Time-of-Flight camera accompanied by audio recorded using both: a microphone array and a microphone built in a mobile computer. For the purpose of applications related to AVSR systems training, every utterance was manually labeled, resulting in label files added to the corpus repository. Owing to the inclusion of recordings made in noisy conditions the elaborated corpus can also be used for testing robustness of speech recognition systems in the presence of acoustic background noise. The process of building the corpus, including the recording, labeling and post-processing phases is described in the paper. Results achieved with the developed audio-visual automatic speech recognition (ASR) engine trained and tested with the material contained in the corpus are presented and discussed together with comparative test results employing a state-of-the-art/commercial ASR engine. In order to demonstrate the practical use of the corpus it is made available for the public use.

Detection, classification and localization of acoustic events in the presence of background noise for acoustic surveillance of hazardous situations

Evaluation of sound event detection, classification and localization of hazardous acoustic events in the presence of background noise of different types and changing intensities is presented. The methods for discerning between the events being in focus and the acoustic background are introduced. The classifier, based on a Support Vector Machine algorithm, is described. The set of features and samples used for the training of the classifier are introduced. The sound source localization algorithm based on the analysis of multichannel signals from the Acoustic Vector Sensor is presented. The methods are evaluated in an experiment conducted in the anechoic chamber, in which the representative events are played together with noise of differing intensity. The results of detection, classification and localization accuracy with respect to the Signal to Noise Ratio are discussed. The results show that the recognition and localization accuracy are strongly dependent on the acoustic conditions. We also found that the engineered algorithms provide a sufficient robustness in moderately intense noise in order to be applied to practical audio-visual surveillance systems.