A fast and accurate &amp;#x201C;shoebox&amp;#x201D; room acoustics simulator

Schimmel, Steven M.; Müller, Martin F.; Dillier, Norbert

doi:10.1109/icassp.2009.4959565

Cited by 27 publications

(22 citation statements)

References 8 publications

(12 reference statements)

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“…The scenes were generated using individual HRTFs and played via speakers located in the ear canals of the test subjects. The virtual environment was simulated using the ROOMSIM software (Schimmel et al, 2009). The simulator uses an image source model to simulate early reflections.…”

Section: Introductionmentioning

confidence: 99%

Localization of virtual sound sources with bilateral hearing aids in realistic acoustical scenes

Mueller

Kegel

Schimmel

et al. 2012

The Journal of the Acoustical Society of America

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Sound localization with hearing aids has traditionally been investigated in artificial laboratory settings. These settings are not representative of environments in which hearing aids are used. With individual Head-Related Transfer Functions (HRTFs) and room simulations, realistic environments can be reproduced and the performance of hearing aid algorithms can be evaluated. In this study, four different environments with background noise have been implemented in which listeners had to localize different sound sources. The HRTFs were measured inside the ear canals of the test subjects and by the microphones of Behind-The-Ear (BTEs) hearing aids. In the first experiment the system for virtual acoustics was evaluated by comparing perceptual sound localization results for the four scenes in a real room with a simulated one. In the second experiment, sound localization with three BTE algorithms, an omnidirectional microphone, a monaural cardioid-shaped beamformer and a monaural noise canceler, was examined. The results showed that the system for generating virtual environments is a reliable tool to evaluate sound localization with hearing aids. With BTE hearing aids localization performance decreased and the number of front-back confusions was at chance level. The beamformer, due to its directivity characteristics, allowed the listener to resolve the front-back ambiguity.

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Section: Introductionmentioning

confidence: 99%

Localization of virtual sound sources with bilateral hearing aids in realistic acoustical scenes

Mueller

Kegel

Schimmel

et al. 2012

The Journal of the Acoustical Society of America

Self Cite

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“…More specifically, the MIT database is used, which contains HRTFs of a KEMAR 1 artificial head that were measured at a distance of 1.4 m in an anechoic chamber [30]. These HRTFs are combined with RIRs, simulated with ROOMSIM, 2 a MATLAB toolbox provided by Schimmel et al [65]. The receiver, KEMAR, was placed at seven different positions in a simulated room of dimensions 6.6×8.6×3 m. For the experiments conducted in this chapter, a set of BRIRs with the following reverberation times are simulated for each of the seven receiver positions, namely, T 60 = {0.2, 0.36, 0.5, 0.62, 0.81 and 1.05 s}.…”

Section: Simulation Of Complex Acoustic Scenesmentioning

confidence: 99%

Binaural Localization and Detection of Speakers in Complex Acoustic Scenes

May

Par

Kohlrausch

2013

The Technology of Binaural Listening

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“…The efficient C++/MATLAB "shoebox" 3D acoustic room simulator ROOMSIM developed by Schimmel et al is selected for simulations [16]. This software takes as input a room dimension (width, depth and height), a source and receiver position and orientation, a receiver's head-related-transfer function (HRTF) model, and frequency-dependent absorption and diffusion coefficients for each surface.…”

Section: Room Simulation and Data Generationmentioning

confidence: 99%

VAST: The Virtual Acoustic Space Traveler Dataset

Gaultier

Kataria

Deleforge

2017

Latent Variable Analysis and Signal Separation

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Abstract. This paper introduces a new paradigm for sound source localization referred to as virtual acoustic space traveling (VAST) and presents a first dataset designed for this purpose. Existing sound source localization methods are either based on an approximate physical model (physics-driven) or on a specific-purpose calibration set (data-driven). With VAST, the idea is to learn a mapping from audio features to desired audio properties using a massive dataset of simulated room impulse responses. This virtual dataset is designed to be maximally representative of the potential audio scenes that the considered system may be evolving in, while remaining reasonably compact. We show that virtually-learned mappings on this dataset generalize to real data, overcoming some intrinsic limitations of traditional binaural sound localization methods based on time differences of arrival.

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A fast and accurate “shoebox” room acoustics simulator

Cited by 27 publications

References 8 publications

Localization of virtual sound sources with bilateral hearing aids in realistic acoustical scenes

Localization of virtual sound sources with bilateral hearing aids in realistic acoustical scenes

Binaural Localization and Detection of Speakers in Complex Acoustic Scenes

VAST: The Virtual Acoustic Space Traveler Dataset

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