A Novel Sound Localization Method Based on Head Related Transfer Function

Ma, Hao; Lin, Zhenzhi; Hu, Hongmei; Wu, Zhenyang

doi:10.1109/icemi.2007.4351175

Cited by 3 publications

(3 citation statements)

References 8 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%

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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%

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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“…Systems based on steered response power electronically steer the microphone array to locate the sound source with the highest power. Another approach is based on extracting spectral features of the Head Related Transfer Function (HRTF) for sound signals received by a dummy head [7]. HRTF based methods give poor accuracy because of its dependence on the individualized characteristics for the HRTFs in addition to the high complexity of analysis.…”

Section: Introductionmentioning

confidence: 99%

Sound source direction estimation in horizontal plane using microphone array

Al-Sheikh

Elshebli

Assaf³

et al. 2013

2013 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)

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Sound Source Localization refers to the ability to identify the location of a certain sound in direction and distance from a reference point. This paper introduces a sound source direction estimation system for an acoustical signal of interest in a 360° sweep in the horizontal plane. In this paper, an array of microphones is used as receivers for the sound waves which are recorded with prescribed blocks size of samples and interfaced to a PC for analysis using digital signal processing techniques. Spectral analysis and wavelet transform are applied on a reference microphone to test if a signal of interest has been received. If so, further processing for signals received on all microphones is initiated to estimate the sound source direction. Generalized Cross Correlation is used in this research for time delay estimation among the microphones since it has low computation complexity, sufficient estimation precision and it works very well in reverberant environments.

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