Literature DB >> 16119357

Extracting the frequencies of the pinna spectral notches in measured head related impulse responses.

Vikas C Raykar1, Ramani Duraiswami, B Yegnanarayana.   

Abstract

The head related impulse response (HRIR) characterizes the auditory cues created by scattering of sound off a person's anatomy. The experimentally measured HRIR depends on several factors such as reflections from body parts (torso, shoulder, and knees), head diffraction, and reflection/ diffraction effects due to the pinna. Structural models (Algazi et al., 2002; Brown and Duda, 1998) seek to establish direct relationships between the features in the HRIR and the anatomy. While there is evidence that particular features in the HRIR can be explained by anthropometry, the creation of such models from experimental data is hampered by the fact that the extraction of the features in the HRIR is not automatic. One of the prominent features observed in the HRIR, and one that has been shown to be important for elevation perception, are the deep spectral notches attributed to the pinna. In this paper we propose a method to robustly extract the frequencies of the pinna spectral notches from the measured HRIR, distinguishing them from other confounding features. The method also extracts the resonances described by Shaw (1997). The techniques are applied to the publicly available CIPIC HRIR database (Algazi et al., 2001c). The extracted notch frequencies are related to the physical dimensions and shape of the pinna.

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Year:  2005        PMID: 16119357     DOI: 10.1121/1.1923368

Source DB:  PubMed          Journal:  J Acoust Soc Am        ISSN: 0001-4966            Impact factor:   1.840


  3 in total

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Journal:  J Neurosci       Date:  2018-03-05       Impact factor: 6.167

2.  Modeling the direction-continuous time-of-arrival in head-related transfer functions.

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Review 3.  An Extended Binaural Real-Time Auralization System With an Interface to Research Hearing Aids for Experiments on Subjects With Hearing Loss.

Authors:  Florian Pausch; Lukas Aspöck; Michael Vorländer; Janina Fels
Journal:  Trends Hear       Date:  2018 Jan-Dec       Impact factor: 3.293

  3 in total

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