Envelope representations of pinna impulse responses relating to three-dimensional localization of sound sources

Hiranaka, Yukio; Yamasaki, Hiro

doi:10.1121/1.388809

Cited by 21 publications

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“…3 (h) and (i) ] from the magnitudes of the corresponding analytical waveforms (Oppenheim and Schafer,. 1975, p. 361 ); this is essentially the method of obtaining the envelopes used by Hiranaka and Yamasaki (1983). Then, the mean values were subtracted and a circular cross correlation was computed [ Fig.…”

Section: B Computation Of Interaural Envelope Delaymentioning

confidence: 99%

Directional dependence of interaural envelope delays

Middlebrooks

Green

1990

The Journal of the Acoustical Society of America

119

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Interaural envelope delays were measured in six human subjects as a function of the location of a movable sound source, bandpassed between 3 and 16 kHz. A total of 324 source locations were tested in horizontal and vertical increments of 10 degrees. A method is described for estimating the complex directional transfer function of the external ear, independent of the position of the recording microphone in the ear canal. To compute interaural envelope delays, directional transfer functions from the left and right ears were convolved with a critical-band filter, the envelopes were computed, and the envelopes were cross correlated. Interaural envelope delays, as well as interaural group delays, varied somewhat with the center frequency of the critical-band filter and with the vertical location of the sound source. Nevertheless, to a first approximation, envelope delays measured in the ear canals increased monotonically with increasing angle of incidence relative to the median plane, as they would for two microphones on the surface of a rigid sphere. The results are discussed in relation to the possible contribution of interaural envelope delays to sound localization behavior.

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Section: B Computation Of Interaural Envelope Delaymentioning

confidence: 99%

Directional dependence of interaural envelope delays

Middlebrooks

Green

1990

The Journal of the Acoustical Society of America

119

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“…Hiranaka and Yamasaki further validated the above phenomena, and found that, all the reflection delays are less than 350 μs for humans through experimentation. Moreover, they also discovered that, when sound source is located in front of the human body, there are at least two reflection waves; at the backside, there is only one; while on top of the head, there are scarcely any reflection components [10]. Wright also confirmed the relationship between HRTFs' characteristics and delay of sound's reflections [11].…”

Section: Introductionmentioning

confidence: 74%

A non-linear spatial hearing model based on bases pursuit algorithm

Zhang

2007

Front. Electr. Electron. Eng. China

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utilizes interaural time difference (ITD) and interaural intensity difference (IID) to interpret the cause of spatial hearing. By virtue of these results, there have been some tentative applications in auditory display of multimedia systems, for instance, VoiceNotes of MIT Media Lab. However, there are still no fully successful models capable of explaining some of the phenomena associated with spatial hearing, such as the perception of a sound's position at an elevation, the "cone of confusion" and so on.In the ensuing research, people take cognizance of the cues implied in grotesque while a little inerratic spectrum. Thus, the systemic description of the entire relevant information, head-related transfer function (HRTF), has been introduced to depict them, where ITD can be seen as the delay between two ears and IID as the difference of HRTFs' magnitude. Moreover, people have constructed different mathematical models to simulate this delicate function, and tried to give a felicitous interpretation for this fancy phenomenon [1−4]. As a popular model structure, the principal component analysis (PCA) model has been greatly studied in this field [2−4]. Basically, researchers in Ref.[2] applied PCA to logarithmic magnitudes of HRTFs that were measured for 10 subjects and at 256 positions. In the PCA model, HRTFs for each position were represented as a weighted combination of a set of basis functions in a low-dimensional subspace, which were obtained from the measured logarithmic magnitudes of HRTFs. While the phase was approximated from the magnitude related to a certain position based on minimum phase characteristics. The spatial feature extraction and regularization model put forward in Ref.[3] contains the phase information during the HRTFs' reduction representation. Furthermore, a thin-plate spline using regulation procedures was also introduced to obtain a mathematical representation for sampled spatial positions. As a result, unmeasured positions' HRTFs can be approximately reestablished from the mathematical equation, which effectively surmount the spatially discrete limitation of HRTFs' measurement. However, both these PCA models were used to process complex valued (amplitude and phase) head-related transfer functions, and needed a lot of complex and logarithmic operations. In Ref.[4], Wu applied Karhunen-Loève transformation to HRIRs in time Abstract In the research on spatial hearing and realization of virtual auditory space, it is important to effectively model the head-related transfer functions (HRTFs) or head-related impulse responses (HRIRs). In our study, we managed to carry out adaptive non-linear approximation in the field of wavelet transformation. The results show that the HRIRs' adaptive non-linear approximation model is a more effective data reduction model, is faster, and is 5 dB on average better than the traditional principal component analysis (PCA) (Karhunen-Loève transform) model based on relative mean square error (MSE) criterion. Furthermore, we also discussed the best bases' choice for t...

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“…The acoustic effects of the big brown bat's external ear on sound reception ͑Jen and Chen, 1988; Wotton et al, 1995 are much like the effects observed in humans ͑Asano et al ., 1990;Batteau, 1967;Blauert, 1983;Butler and Belendiuk, 1977;Djupesland and Zwislocki, 1973;Fisher and Freedman, 1968;Flynn and Elliott, 1965;Hebrank and Wright, 1974;Hiranaka and Yamasaki, 1983;Middlebrooks, 1992;Middlebrooks and Green, 1991;Shaw, 1982;Shaw and Teranishi, 1968;Wightman and Kistler, 1989;Wright et al, 1974͒, in cats ͑Calford andPettigrew, 1984;Musicant et al, 1990;Phillips et al, 1982;Rice et al, 1992͒, in guinea pigs ͑Carlile andPettigrew, 1987͒, and in ferrets ͑Car-lile, 1990͒. In each case, the location and strength of a welldefined peak-and-notch pattern in the spectrum of sounds reaching the eardrum depends on the elevation of the sound source.…”

Section: Introductionmentioning

confidence: 93%

Transformation of external-ear spectral cues into perceived delays by the big brown bat, Eptesicus fuscus

Simmons

Wotton

Ferragamo

et al. 2002

The Journal of the Acoustical Society of America

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The external-ear transfer function for big brown bats (Eptesicus fuscus) contains two prominent notches that vary from 30 to 55 kHz and from 70 to 100 kHz, respectively, as sound-source elevation moves from -40 to +10 degrees. These notches resemble a higher-frequency version of external-ear cues for vertical localization in humans and other mammals. However, they also resemble interference notches created in echoes when reflected sounds overlap at short time separations of 30-50 micros. Psychophysical experiments have shown that bats actually perceive small time separations from interference notches, and here we used the same technique to test whether external-ear notches are recognized as a corresponding time separation, too. The bats' performance reveals the elevation dependence of a time-separation estimate at 25-45 micros in perceived delay. Convergence of target-shape and external-ear cues onto echo spectra creates ambiguity about whether a particular notch relates to the object or to its location, which the bat could resolve by ignoring the presence of notches at external-ear frequencies. Instead, the bat registers the frequencies of notches caused by the external ear along with notches caused by the target's structure and employs spectrogram correlation and transformation (SCAT) to convert them all into a family of delay estimates that includes elevation.

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Envelope representations of pinna impulse responses relating to three-dimensional localization of sound sources

Cited by 21 publications

References 0 publications

Directional dependence of interaural envelope delays

Directional dependence of interaural envelope delays

A non-linear spatial hearing model based on bases pursuit algorithm

Transformation of external-ear spectral cues into perceived delays by the big brown bat, Eptesicus fuscus

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