A modular intelligent transportation system, comprising an environmentally protected enclosure, a system communications bus, a processor module, communicating with said bus, having a image data input and an audio input, the processor module analyzing the image data and/or audio input for data patterns represented therein, having at least one available option slot, a power supply, and a communication link for external communications, in which at least one available option slot can be occupied by a wireless local area network access point, having a communications path between said communications link and said wireless access point, or other modular components.

Modular intelligent transportation system

This paper presents the design and implementation of the HARK robot audition software system consisting of sound source localization modules, sound source separation modules and automatic speech re...

Design and Implementation of Robot Audition System 'HARK' — Open Source Software for Listening to Three Simultaneous Speakers

Localization of sound sources in robotics: A review

This paper addresses auditory scene analysis, especially, sound source localization using an aerial vehicle with a microphone array in an outdoor environment. Since such a vehicle is able to search sound sources quickly and widely, it is useful to detect outdoor sound sources, for instance, to find distressed people in a disaster situation. In such an environment, noise is quite loud and dynamically-changing, and conventional microphone array techniques studied in the field of indoor robot audition are of less use. We, thus, proposed MUltiple SIgnal Classification based on incremental Generalized EigenValue Decomposition (iGEVD-MUSIC). It can deal with high power noise by introducing a noise correlation matrix and GEVD even when the signal-to-noise ratio is less than 0 dB. In addition, the noise correlation matrix is incrementally estimated to adapt to dynamic changes in noise. We developed a prototype system for auditory scene analysis based on the proposed method using the Parrot AR.Drone with a microphone array and a Kinect device. Experimental results using the prototype system showed that dynamically-changing noise is properly suppressed with the proposed method and multiple human voice sources are able to be localized even when the AR.Drone is moving in an outdoor environment.

http://www.jp.honda-ri.com/upload/document/entry/20121016/Okutani_2012_939_6210.pdf

Outdoor auditory scene analysis using a moving microphone array embedded in a quadrocopter

This study presents a smart homecare surveillance system, which utilizes sound-steered cameras to identify behavior of interest. First of all, to detect multiple source locations, a new direction-of-arrival (DOA) algorithm is proposed by introducing cascaded frequency filters, which can quickly calculate directions without creating much complexity. This method can also locate and separate different signals at the same time. Second, after the camera points in the direction of the estimated angle, the proposed state-transition support vector machine is used to provide favorable discriminability for human behavior identification. A new Markov random field (MRF) function based on the localized contour sequence (LCS) is also presented while the system computes transition probabilities between states. Such LCS-based MRF functions can effectively smooth transitions and enhance recognition. The experimental results show that the average error of DOA decreases to around 7°, which is better than those of the baselines. Also, our proposed behavior identification system can reach an 88.3% accuracy rate. The aforementioned results have therefore demonstrated the feasibility of the proposed method.

Smart Homecare Surveillance System: Behavior Identification Based on State-Transition Support Vector Machines and Sound Directivity Pattern Analysis

For cooperative work of robots and humans in the real world, a communicative function based on speech is indispensable for robots. To realize such a function in a noisy real environment, it is essential that robots be able to extract target speech spoken by humans from a mixture of sounds by their own resources. We have developed a method of detecting and extracting speech events based on the fusion of audio and video information. In this method, audio information (sound localization using a microphone array) and video information (human tracking using a camera) are fused by a Bayesian network to enable the detection of speech events. The information of detected speech events is then utilized in sound separation using adaptive beam forming. In this paper, some basic investigations for applying the above system to the humanoid robot HRP-2 are reported. Input devices, namely a microphone array and a camera, were mounted on the head of HRP-2, and acoustic characteristics for sound localization/separation performance were investigated. Also, the human tracking system was improved so that it can be used in a dynamic situation. Finally, overall performance of the system was tested via off-line experiments.

K. Yamamoto

Papers

Robust speech interface based on audio and video information fusion for humanoid HRP-2