Erratum: ‘‘Influence of monaural spectral cues on binaural localization’’ [J. Acoust. Soc. Am. 77, 202–208 (1985)].

1985 ◽  
Vol 77 (6) ◽  
pp. 2190-2190
Author(s):  
Alan D. Musicant ◽  
Robert A. Butler
Acta Acustica ◽  
2020 ◽  
Vol 4 (5) ◽  
pp. 21
Author(s):  
Song Li ◽  
Robert Baumgartner ◽  
Jürgen Peissig

Perceived externalization is a relevant feature to create an immersive acoustic environment with headphone reproduction. In the present study, listener-specific acoustic transfer characteristics for an azimuth angle of 90° were modified to investigate the role of monaural spectral cues, interaural level differences (ILDs), and temporal fluctuations of ILDs on perceived externalization in anechoic and reverberant environments. Listeners’ ratings suggested that each acoustic cue was important for perceived externalization. If only one correct acoustic cue remained in the ear signals, the sound image could not be perceived as fully externalized. Reverberation did reduce but not eliminate the influences of monaural spectral and ILD cues on perceived externalization. Additionally, the spectral details of the ipsilateral ear signal were more important for perceived externalization than those in the contralateral ear signal. A computational model was proposed to quantify those relationships and predict externalization ratings by comparing the acoustic cues extracted from the target (modified) and template (non-processed) binaural signals after several auditory processing steps. The accuracy of predicted externalization ratings was higher than 90% under all experimental conditions.


1994 ◽  
Vol 71 (6) ◽  
pp. 2194-2216 ◽  
Author(s):  
F. K. Samson ◽  
P. Barone ◽  
J. C. Clarey ◽  
T. J. Imig

1. Single-unit recordings were carried out in primary auditory cortex (AI) of barbiturate-anesthetized cats. Observations were based on a sample of 131 high-best-frequency (> 5 kHz), azimuth-sensitive neurons. These were identified by their responses to a set of noise bursts, presented in the free field, that varied in azimuth and sound-pressure level (SPL). Each azimuth-sensitive neuron responded well to some levels at certain azimuths, but did not respond well to any level at other azimuths. 2. Unilateral ear plugging was used to infer each neuron's response to monaural stimulation. Ear plugs, produced by injecting a plastic ear mold compound into the external ear, attenuated sound reaching the tympanic membrane by 25–70 dB. The azimuth tuning of a large proportion of the sample (62/131), referred to as binaural directional (BD), was completely dependent upon binaural stimulation because with one ear plugged, these cells were insensitive to azimuth (either responded well at all azimuths or failed to respond at any azimuth) or in a few cases exhibited striking changes in location of azimuth function peaks. This report describes patterns of monaural responses and binaural interactions exhibited by BD neurons and relates them to each cell's azimuth and level tuning. The response of BD cells to ear plugging is consistent with the hypothesis that they derive azimuth tuning from interaural level differences present in noise bursts. Another component of the sample consisted of monaural directional (27/131) cells that derived azimuth tuning in part or entirely from monaural spectral cues. Cells in the remaining portion of the sample (42/131) responded too unreliably to permit specific conclusions. 3. Binaural interactions were inferred by statistical comparison of a cell's responses to monaural (unilateral plug) and binaural (no plug) stimulation. A larger binaural response than either monaural response was taken as evidence for binaural facilitation. A smaller binaural than monaural response was taken as evidence for binaural inhibition. Binaural facilitation was exhibited by 65% (40/62) of the BD sample (facilitatory cells). Many of these exhibited mixed interactions, i.e., binaural facilitation occurred in response to some azimuth-level combinations, and binaural inhibition to others. Binaural inhibition in the absence of binaural facilitation occurred in 35% (22/62) of the BD sample, a majority of which were EI cells, so called because they received excitatory (E) input from one ear (excitatory ear) and inhibitory (I) input from the other (inhibitory ear). One cell that exhibited binaural inhibition received excitatory input from each ear.(ABSTRACT TRUNCATED AT 400 WORDS)


2000 ◽  
Vol 84 (3) ◽  
pp. 1330-1345 ◽  
Author(s):  
Frank K. Samson ◽  
Pascal Barone ◽  
W. Andrew Irons ◽  
Janine C. Clarey ◽  
Pierre Poirier ◽  
...  

Azimuth tuning of high-frequency neurons in the primary auditory cortex (AI) is known to depend on binaural disparity and monaural spectral (pinna) cues present in broadband noise bursts. Single-unit response patterns differ according to binaural interactions, strength of monaural excitatory input from each ear, and azimuth sensitivity to monaural stimulation. The latter characteristic has been used as a gauge of neural sensitivity to monaural spectral directional cues. Azimuth sensitivity may depend predominantly on binaural disparity cues, exclusively on monaural spectral cues, or on both. The primary goal of this study was to determine whether each cortical response pattern corresponds to a similar pattern in the medial geniculate body (MGB) or whether some patterns are unique to the cortex. Single-unit responses were recorded from the ventral nucleus (Vn) and lateral part of the posterior group of thalamic nuclei (Po), tonotopic subdivisions of the MGB. Responses to free-field presentation of noise bursts that varied in azimuth and sound pressure level were obtained using methods identical to those used previously in field AI. Many units were azimuth sensitive, i.e., they responded well at some azimuths, and poorly, if at all, at others. These were studied further by obtaining responses to monaural noise stimulation, approximated by reversible plugging of one ear. Monaural directional (MD) cells were sensitive to the azimuth of monaural noise stimulation, whereas binaural directional (BD) cells were either insensitive to its azimuth or monaurally unresponsive. Thus BD and MD cells show differential sensitivity to monaural spectral cues. Monaural azimuth sensitivity could not be used to interpret the spectral sensitivity of predominantly binaural cells that exhibited strong binaural facilitation because they were either unresponsive or poorly responsive to monaural stimulation. The available evidence suggests that some such cells are sensitive to spectral cues. The results do not indicate the presence of any response types in AI that are not present in the MGB. Vn and Po contain similar classes of MD and BD cells. Because Po neurons project to the anterior auditory field, neurons in this cortical area also are likely to exhibit differential sensitivity to binaural disparity and monaural spectral cues. Comparison of these MGB data with a published report of cochlear nucleus (CN) single-unit azimuth tuning shows that MGB sensitivity to spectral cues is considerably stronger than CN sensitivity.


2020 ◽  
Author(s):  
Andrew Francl ◽  
Josh H. McDermott

AbstractMammals localize sounds using information from their two ears. Localization in real-world conditions is challenging, as echoes provide erroneous information, and noises mask parts of target sounds. To better understand real-world localization we equipped a deep neural network with human ears and trained it to localize sounds in a virtual environment. The resulting model localized accurately in realistic conditions with noise and reverberation, outperforming alternative systems that lacked human ears. In simulated experiments, the network exhibited many features of human spatial hearing: sensitivity to monaural spectral cues and interaural time and level differences, integration across frequency, and biases for sound onsets. But when trained in unnatural environments without either reverberation, noise, or natural sounds, these performance characteristics deviated from those of humans. The results show how biological hearing is adapted to the challenges of real-world environments and illustrate how artificial neural networks can extend traditional ideal observer models to real-world domains.


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