Modeling non-individualized binaural sound localization in the horizontal plane using artificial neural networks

Alim, O.A.; Farag, Hania H.

doi:10.1109/ijcnn.2000.861395

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…In attempting to develop more biologically inspired approaches, a number of researchers have also proposed ANN based models (Palmieri et al, 1991; Backman and Karjalainen, 1993; Datum et al, 1996; Abdel Alim and Farag, 2000; Chung et al, 2000; Hao et al, 2007; Keyrouz and Diepold, 2008). As with their computational counterparts, it has been demonstrated that such networks can be used to localize sound sources.…”

Section: Models Of Sound Localizationmentioning

confidence: 99%

“…A number of computational models utilizing various mathematical techniques for sound localization have been reported in the literature (Huang et al, 1999 ; Handzel and Krishnaprasad, 2002 ; Nakashima et al, 2003 ; Willert et al, 2006 ; Keyrouz and Diepold, 2008 , Li et al, 2009 ; Murray et al, 2009 ). In attempting to develop more biologically inspired approaches, a number of researchers have also proposed ANN based models (Palmieri et al, 1991 ; Backman and Karjalainen, 1993 ; Datum et al, 1996 ; Abdel Alim and Farag, 2000 ; Chung et al, 2000 ; Hao et al, 2007 ; Keyrouz and Diepold, 2008 ). As with their computational counterparts, it has been demonstrated that such networks can be used to localize sound sources.…”

Section: Models Of Sound Localizationmentioning

confidence: 99%

“…The length of each delay indicated in Figure 7 was determined using a simple formula devised by Lord Rayleigh (Abdel Alim and Farag, 2000 ). He considered a sound wave traveling at the speed of sound, c = 343 m/s, which makes contact with a spherical head of radius r from a direction at an angle θ.…”

Section: Snn Model Of the Msomentioning

confidence: 99%

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A spiking neural network model of the medial superior olive using spike timing dependent plasticity for sound localization

Glackin¹

2010

Front.Comput.Neurosci.

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Sound localization can be defined as the ability to identify the position of an input sound source and is considered a powerful aspect of mammalian perception. For low frequency sounds, i.e., in the range 270 Hz–1.5 KHz, the mammalian auditory pathway achieves this by extracting the Interaural Time Difference between sound signals being received by the left and right ear. This processing is performed in a region of the brain known as the Medial Superior Olive (MSO). This paper presents a Spiking Neural Network (SNN) based model of the MSO. The network model is trained using the Spike Timing Dependent Plasticity learning rule using experimentally observed Head Related Transfer Function data in an adult domestic cat. The results presented demonstrate how the proposed SNN model is able to perform sound localization with an accuracy of 91.82% when an error tolerance of ±10° is used. For angular resolutions down to 2.5°, it will be demonstrated how software based simulations of the model incur significant computation times. The paper thus also addresses preliminary implementation on a Field Programmable Gate Array based hardware platform to accelerate system performance.

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Section: Models Of Sound Localizationmentioning

confidence: 99%

Section: Models Of Sound Localizationmentioning

confidence: 99%

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A spiking neural network model of the medial superior olive using spike timing dependent plasticity for sound localization

Glackin¹

2010

Front.Comput.Neurosci.

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A Novel Sound Localization Method Based on Head Related Transfer Function

Ma¹,

Lin²,

Hu³

et al. 2007

2007 8th International Conference on Electronic Measurement and Instruments

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Interpolation of Head-Related Transfer Functions Using Neural Network

Zhong

2013

2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics

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Spatial interpolation of head-related transfer functions (HRTFs) is necessary to reconstruct the spatially continuous HRTF function and therefore virtual sound sources in virtual auditory displays. On the basis of the artificial neural network with radial basis functions, this paper proposes a nonlinear interpolation method for HRTFs. Performance of the proposed interpolation method was validated using a highresolution HRTF database with the directional resolution of 1°. Computational results indicate that the mean signal distortion ratio is 50.8 dB, 41.7 dB, 36.1 dB, 32.1 dB, 28.8 dB, 20.4 dB, and 16.9 dB for azimuthal intervals of 2°, 4°, 6°, 8°, 10°, 20°, and 30°, respectively. Moreover, the interpolation performance is better for the ipsilateral HRTFs compared with the contralateral HRTFs.

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Modeling non-individualized binaural sound localization in the horizontal plane using artificial neural networks

Cited by 5 publications

References 7 publications

A spiking neural network model of the medial superior olive using spike timing dependent plasticity for sound localization

A spiking neural network model of the medial superior olive using spike timing dependent plasticity for sound localization

A Novel Sound Localization Method Based on Head Related Transfer Function

Interpolation of Head-Related Transfer Functions Using Neural Network

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