Auditory Evidence Grids

Martinson, Eric; Schultz, Alan C.

doi:10.1109/iros.2006.281843

Cited by 40 publications

(43 citation statements)

References 7 publications

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“…[15], where they looked at sounds from movies, such as dog barks, car horns, and violent content. Another example is from Martinson and Schultz [16], where a robot classifies voice signatures to help identify people. In all of these works, features were extracted by looking at short windows in time and characterizing the temporal and spectral characteristics of these windows.…”

Section: Related Work In Acousticsmentioning

confidence: 99%

Using sound to classify vehicle-terrain interactions in outdoor environments

Libby

Stentz

2012

2012 IEEE International Conference on Robotics and Automation

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Abstract-Robots that operate in complex physical environments can improve the accuracy of their perception systems by fusing data from complementary sensing modalities. Furthermore, robots capable of motion can physically interact with these environments, and then leverage the sensory information they receive from these interactions. This paper explores the use of sound data as a new type of sensing modality to classify vehicle-terrain interactions from mobile robots operating outdoors, which can complement more typical non-contact sensors that are used for terrain classification. Acoustic data from microphones was recorded on a mobile robot interacting with different types of terrains and objects in outdoor environments. This data was then labeled and used offline to train a supervised multiclass classifier that can distinguish between these interactions based on acoustic data alone. To the best of the author's knowledge, this is the first time that acoustics has been used to classify a variety of interactions that a vehicle can have with its environment, so part of our contribution is to survey acoustic techniques from other domains and explore their efficacy for this application. The feature extraction methods we implement are derived from this survey, which then serve as inputs to our classifier. The multiclass classifier is then built from Support Vector Machines (SVMs). The results presented show an average of 92% accuracy across all classes, which suggest strong potential for acoustics to enhance perception systems on mobile robots.

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Section: Related Work In Acousticsmentioning

confidence: 99%

Using sound to classify vehicle-terrain interactions in outdoor environments

Libby

Stentz

2012

2012 IEEE International Conference on Robotics and Automation

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“…classification vector). In this section, we present the auditory evidence grid algorithm (Martinson and Schultz 2006) for localizing sound sources in two or more dimensions. While other robotic strategies have been used for localizing sound sources in two dimensions relative to the robot (Nakadai et al 2006;Sasaki et al 2006), the advantage of auditory evidence grids is their simplicity to implement in software and hardware.…”

Section: Localizing the Sound Sourcesmentioning

confidence: 99%

Discovery of sound sources by an autonomous mobile robot

Martinson

Schultz

2009

Auton Robot

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In this work, we describe an autonomous mobile robotic system for finding, investigating, and modeling ambient noise sources in the environment. The system has been fully implemented in two different environments, using two different robotic platforms and a variety of sound source types. Making use of a two-step approach to autonomous exploration of the auditory scene, the robot first quickly moves through the environment to find and roughly localize unknown sound sources using the auditory evidence grid algorithm. Then, using the knowledge gained from the initial exploration, the robot investigates each source in more depth, improving upon the initial localization accuracy, identifying volume and directivity, and, finally, building a classification vector useful for detecting the sound source in the future.

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“…Another aspect of mobile robot audition, sound field measurement and re-use for mobile robots is proposed [8].…”

Section: Related Workmentioning

confidence: 99%

Loudness measurement of human utterance to a robot in noisy environment

Kagami

Sasaki

Thompson

et al. 2008

Proceedings of the 3rd ACM/IEEE International Conference on Human Robot Interaction

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In order to understand utterance based human-robot interation, and to develop such a system, this paper initially analyzes how loud humans speak in a noisy environment. Experiments were conducted to measure how loud humans speak with 1) different noise levels, 2) different number of sound sources, 3) different sound sources, and 4) different distances to a robot. Synchronized sound sources add noise to the auditory scene, and resultant utterances are recorded and compared to a previously recorded noiseless utterance. From experiments, we understand that humans generate basically the same level of sound pressure level at his/her location irrespective of distance and background noise. More precisely, there is a band according to a distance, and also according to sound sources that is including language pronounce.According to this understanding, we developed an online spoken command recognition system for a mobile robot. System consists of two key componenets: 1) Low side-lobe microphone array that works as ominidirectional telescopic microphone, and 2) DSBF combined with FBS method for sound source localization and segmentation. Caller location and segmented sound stream are calculated, and then the segmented sound stream is sent to voice recognition system. The system works with at most five sound sources at the same time with about at most 18[dB] sound pressure differences. Experimental results with the modile robot are also shown.

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Auditory Evidence Grids

Cited by 40 publications

References 7 publications

Using sound to classify vehicle-terrain interactions in outdoor environments

Using sound to classify vehicle-terrain interactions in outdoor environments

Discovery of sound sources by an autonomous mobile robot

Loudness measurement of human utterance to a robot in noisy environment

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