Acoustic source location in a three-dimensional space using crosspower spectrum phase

Svaizer, Piergiorgio; Matassoni, Marco; Omologo, Maurizio

doi:10.1109/icassp.1997.599611

Cited by 69 publications

(46 citation statements)

References 7 publications

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“…Most researchers use an array of four or more microphones to perform auditory source localization [1], [154], [161], [163]. Such a system is too expensive to compute a signal processor, too complex and also too energy consuming, despite the fact that it can detect the signals in three-dimensional space and precisely localize the source.…”

Section: A Stereo Auditory Sensormentioning

confidence: 99%

Neural Preprocessing and Control of Reactive Walking Machines

Manoonpong¹

2007

Cognitive Technologies

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Research in the domain of biologically inspired walking machines has focused for the most part on the mechanical designs and locomotion control. Although some of this research has been concentrated on the generation of a reactive behavior of walking machines, it has been restricted only to a few of such reactive behaviors. However, from this research, there are only few examples where different behaviors have been implemented in one machine at the same time. In general, these walking machines were solely designed for pure locomotion, i.e. without sensing environmental stimuli.Therefore, in this thesis, biologically inspired walking machines with different reactive behaviors are presented. Inspired by obstacle avoidance and escape behavior of scorpions and cockroaches, such behavior is implemented in the walking machines as a negative tropism. On the other hand, a sound induced behavior called "sound tropism", in analogy to the prey capture behavior of spiders, is employed as a model of a positive tropism. The biological sensing systems which those animals use to trigger the described behaviors are investigated so that they can be reproduced in the abstract form with respect to their principle functionalities. In addition, the morphologies of a salamander and a cockroach which are designed for efficient locomotion are also taken into account for the leg and trunk designs of the four-and six-legged walking machines, respectively. Different behavior controls for generating the biologically inspired reactive behaviors are developed on the basis of a modular neural structure. Each behavior control consists of a neural preprocessing module and a neural control module. Preprocessing is for sensory signals while the neural control generates basic locomotion and changes the appropriate motions, e.g. turning left, right or walking backward, with respect to sensory signals. Neural preprocessing and control are formed by realizing discrete-time dynamical properties of recurrent neural networks. Parts of the networks are generated iii iv Abstract and optimized by using an evolutionary algorithm. Utilizing the modular neural structure, the coupling of the neural control module with different neural preprocessing modules leads to the desired behavior controllers, e.g. obstacle avoidance and sound tropism. Furthermore, these behavior controllers are then fused by using a sensor fusion technique consisting of lookup table and time scheduling methods to obtain an effective behavior fusion controller, whereby different neural preprocessing modules have to cooperate.Eventually, all of these reactive behavior controllers together with the physical sensor systems are implemented on the physical walking machines to be tested in a real world environment. The fully equipped walking machines can be seen as artificial perception-action systems. As a result, the walking machine(s) is able to respond to environmental stimuli, e.g. wandering around, sound tropism (positive tropism), avoiding obstacles and even escaping from corners a...

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Section: A Stereo Auditory Sensormentioning

confidence: 99%

Neural Preprocessing and Control of Reactive Walking Machines

Manoonpong¹

2007

Cognitive Technologies

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“…PHAT is very robust to noise and reverberations compared to other correlation based TDE techniques (J. DiBiase, 2001;Svaizer et al, 1997). Let X k (ω) be the Fourier transform of microphone k. The GCC between microphone l and m is…”

Section: Time-delay Estimationmentioning

confidence: 99%

Camera Pointing with Coordinate-Free Localization and Tracking

Ettinger¹,

Freund²

2011

Advances in Sound Localization

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where c is the speed of sound in the medium. ∆ ij is often called the time delay of arrival (TDOA) between microphone i and j. It is worth noting that if f is the sampling rate being used, then the largest the TDOA can be in terms of audio samples is M = m i − m j 2 f /c. In other words, ∆ ij is always in the range [−M, M] and in practice can only be estimated to the nearest sample. This observation directly reveals the fact that close together microphones cannot have as wide a range of TDOAs as microphones that are spaced further apart. Placing microphones further 318 Advances in Sound Localization

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“…It is perhaps informative to think of these co-occurrence values in the context of audio localization [17]. In audio localization the time difference of arrival (TDOA) is estimated by finding the peak of the cross-correlation between two audio signals.…”

Section: Relationship To Geometrymentioning

confidence: 99%