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Brian C J Moore - One of the best experts on this subject based on the ideXlab platform.
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using a deep neural network to speed up a model of Loudness for time varying sounds
Proceedings of the International Symposium on Auditory and Audiological Research, 2019Co-Authors: Josef Schlittenlacher, Richard Turner, Brian C J MooreAbstract:The “time-varying Loudness (TVL)” model calculates “instantaneous Loudness” every 1 ms, and this is used to generate predictions of short-term Loudness, the Loudness of a short segment of sound such as a word in a sentence, and of long-term Loudness, the Loudness of a longer segment of sound, such as a whole sentence. The calculation of instantaneous Loudness is computationally intensive and real-time implementation of the TVL model is difficult. To speed up the computation, a deep neural network (DNN) has been trained to predict instantaneous Loudness using a large database of speech sounds and artificial sounds (tones alone and tones in white or pink noise), with the predictions of the TVL model as a reference (providing the "correct" answer, specifically the Loudness level in phons). A multilayer perceptron with three hidden layers was found to be sufficient, with more complex DNN architecture not yielding higher accuracy. After training, the deviations between the predictions of the TVL model and the predictions of the DNN were typically less than 0.5 phons, even for types of sounds that were not used for training (music, rain, animal sounds, washing machine). The DNN calculates instantaneous Loudness over 100 times more quickly than the TVL model.
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effectiveness of a Loudness model for time varying sounds in equating the Loudness of sentences subjected to different forms of signal processing
Journal of the Acoustical Society of America, 2016Co-Authors: Tudorcătălin Zorilă, Yannis Stylianou, Sheila Flanagan, Brian C J MooreAbstract:A model for the Loudness of time-varying sounds [Glasberg and Moore (2012). J. Audio. Eng. Soc. 50, 331–342] was assessed for its ability to predict the Loudness of sentences that were processed to either decrease or increase their dynamic fluctuations. In a paired-comparison task, subjects compared the Loudness of unprocessed and processed sentences that had been equalized in (1) root-mean square (RMS) level; (2) the peak long-term Loudness predicted by the model; (3) the mean long-term Loudness predicted by the model. Method 2 was most effective in equating the Loudness of the original and processed sentences.
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A revised model of Loudness perception applied to cochlear hearing loss.
Hearing research, 2004Co-Authors: Brian C J Moore, Brian R GlasbergAbstract:We previously described a model for Loudness perception for people with cochlear hearing loss. However, that model is incompatible with our most recent and most satisfactory model of Loudness for normal hearing. Here, we describe a Loudness model that is applicable to both normal and impaired hearing. In contrast to our earlier model for impaired hearing, the new model correctly predicts: (1) that a sound at absolute threshold has a small but finite Loudness; (2) that, for levels very close to the absolute threshold, the rate of growth of Loudness is similar for normal ears and ears with cochlear hearing loss; (3) the relation between monaural and binaural threshold and Loudness; (4) recent measures of equal-Loudness contours. Like the earlier model, the new model can account for the Loudness recruitment and reduced Loudness summation that are typically associated with cochlear hearing loss.
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a model of Loudness applicable to time varying sounds
Journal of The Audio Engineering Society, 2002Co-Authors: Brian R Glasberg, Brian C J MooreAbstract:Previously we described a model for calculating the Loudness of steady sounds from their spectrum. Here a new version of the model is presented, which uses a waveform as its input. The stages of the model are as follows. (a) A finite impulse response filter representing transfer through the outer and middle ear. (b) Calculation of the short-term spectrum using the fast Fourier transform (FFT). To give adequate spectral resolution at low frequencies, combined with adequate temporal resolution at high frequencies, six FFTs are calculated in parallel, using longer signal segments for low frequencies and shorter segments for higher frequencies. (c) Calculation of an excitation pattern from the physical spectrum. (d) Transformation of the excitation pattern to a specific Loudness pattern. (e) Determination of the area under the specific Loudness pattern. This gives a value for the instantaneous Loudness. The short-term perceived Loudness is calculated from the instantaneous Loudness using an averaging mechanism similar to an automatic gain control system, with attack and release times. Finally the overall Loudness impression is calculated from the short-term Loudness using a similar averaging mechanism, but with longer attack and release times. The new model gives very similar predictions to our earlier model for steady sounds. In addition, it can predict the Loudness of brief sounds as a function of duration and the overall Loudness of sounds that are amplitude modulated at various rates.
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a model for the prediction of thresholds Loudness and partial Loudness
Journal of The Audio Engineering Society, 1997Co-Authors: Brian C J Moore, Brian R Glasberg, Thomas BaerAbstract:A Loudness model for steady sounds is described having the following stages: 1) a fixed filter representing transfer through the outer ear; 2) a fixed filter representing transfer through the middle ear; 3) calculation of an excitation pattern from the physical spectrum; 4) transformation of the excitation pattern to a specific Loudness pattern; 5) determination of the area under the specific Loudness pattern, which gives overall Loudness for a given ear; and 6) summation of Loudness across ears. The model differs from earlier models in the following areas: 1) the assumed transfer function for the outer and middle ear; 2) the way that excitation patterns are calculated; 3) the way that specific Loudness is related to excitation for sounds in quiet and in noise; and (4) the way that binaural Loudness is calculated from monaural Loudness. The model is based on the assumption that sounds at absolute threshold have a small but finite Loudness. This Loudness is constant regardless of frequency and spectral content. It is also assumed that a sound at masked threshold has the same Loudness as a sound at absolute threshold. The model accounts well for recent measures of equal-Loudness contours, which differ from earlier measures because of improved control over bias effects. The model correctly predicts the relation between monaural and binaural threshold and Loudness. It also correctly accounts for the threshold and Loudness of complex sounds as a function of bandwidth.
Sabine Meunier - One of the best experts on this subject based on the ideXlab platform.
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BINAURAL Loudness OF MOVING SOURCES IN FREE FIELD: PERCEPTUAL MEASUREMENTS VERSUS AT-EAR LEVELS
2018Co-Authors: Sabine Meunier, Jacques Chatron, Sophie Savel, Guy RabauAbstract:Most investigations on the variations of Loudness with the spatial position of a sound source have been made for static sounds. The purpose of this work was to study the Loudness of a moving source. By analogy with studies on difference in Loudness between sounds increasing or decreasing in intensity (without movement of the source), we studied the global Loudness of a moving sound. The analogy with the sounds whose intensity varies is direct because the at-ear level depends on the position of the source, so a moving sound will create levels that vary over time at the entrance of the auditory canal. We measured the overall Loudness of a moving source as a function of the starting and ending positions of the stimulus and of its direction of rotation. Overall, we did not find any overall Loudness difference according to the direction of variation of the source. Moreover, the results obtained with a static sound seem to confirm, with absolute magnitude estimation, the amount of directional Loudness sensitivity measured previously with an adaptive method. In free field, Loudness depends on the position of the sound source (Sivonen and Ellermeier, 2006). In order to quantify the effect of the incidence angle on Loudness, the directional Loudness sensitivity (DLS) is measured. DSL is defined as the level difference required for equal Loudness between a frontal reference sound (azimuth 0°, elevation 0°) and a test sound at a given position. A negative DLS means that the test sound has been perceived softer than the frontal sound and vice versa. In a previous study, we showed a decrease in DLS with an increase in azimuth of an amount of more than 10 dB on average (25 listeners, Meunier et al., 2016). Different studies have also examined the Loudness of sounds that increase and sounds that decrease in level. For sounds that only differ in temporal envelop, it has been shown that the global Loudness of a sound whose level increases is greater than the global Loudness of a sound whose level decreases (Ponsot et al., 2015a, 2015b). This phenomenon has been called asymmetry in Loudness. When a sound source is moving around a listener, the at-ear level of the sound varies. Moreover, if we refer to the studies on directional Loudness, its Loudness should also vary. The aim of the work presented here was to explore how global Loudness of moving sounds is formed and the main point was to determine whether there is an asymmetry between sounds that move in opposite directions as their level and Loudness also vary in opposite directions.
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Loudness Processing of Time-Varying Sounds: Recent advances in psychophysics and challenges for future research
2016Co-Authors: Emmanuel Ponsot, Patrick Susini, Sabine MeunierAbstract:Current Loudness models and indicators of time-varying sounds such as the 95th percentile of the Loudness distribution N 5 all compute global Loudness from short-term Loudness time-series by leaving the temporal dimension out. Putting it differently, these models all rely on the assumption that the global Loudness of a time-varying sound does not depend on the shape of its Loudness pattern, i.e. that it can be predicted by its Loudness distribution only. This assumption has been challenged and invalidated by a number of recent studies investigating time-varying sounds with both flat (e.g., fluctuating stimuli) and non-flat (e.g., rising-or falling-intensity stimuli) overall intensity profiles. We will intend to review the main outcomes of these studies and illustrate the implication of various high-level processes (cognitive, memory) in overall Loudness evaluations of time-varying sounds, not yet considered in current Loudness models. Then, we will discuss how the association of " molar " and " molecular " (i.e. reverse-correlation) psychophysics could help characterize and identify the whole underlying perceptual machinery.
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speech and audio Loudness depending on telephone audio bandwidth and codec a subjective testing approach
International Conference on Acoustics Speech and Signal Processing, 2014Co-Authors: Idir Edjekouane, Cyril Plapous, Catherine Quinquis, Sabine MeunierAbstract:In this paper, we propose a new approach for the subjective assessment of the Loudness of complex audio signals such as speech or music. This two-stage approach makes it possible to study the influence on Loudness of the frequency bandwidth and of different kinds of codecs. In the first stage, the individual Loudness function of each subject is estimated using a specific 100-point response scale. In the second stage, the subject evaluates the Loudness of each processed sample, by filtering or coding/decoding, using the same scale. The Loudness obtained in terms of points is then converted in Loudness levels in terms of phons using the estimated individual Loudness function. Results show that Loudness increases with the bandwidth extension up to super-wideband. Similar behavior is observed when codecs are applied.
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An overview of Bertram Scharf's research in France on Loudness adaptation
2013Co-Authors: Sabine MeunierAbstract:Since 1978, Professor Bertram Scharf divided his time between the USA and France. He was a Visiting Scientist at the Laboratoire de Mécanique et d'Acoustique in Marseille until the mid-1990s and collaborated with the University of Marseille (Faculté de Médecine) until his death. One of Bertram Scharf's major contributions to the field of psychoacoustics is in the area of Loudness. He first studied spectral Loudness summation, when he started working at Harvard University. In France, his work on Loudness focused mainly on Loudness adaptation. He wrote, "Loudness resembles pain in that it decreases as a function of time only under special stimulus conditions." Bertram Scharf's work with his French colleagues defined aspects of Loudness adaptation in its direct (simple Loudness adaptation) and indirect (induced Loudness adaptation) forms. They studied how the auditory system recovers from Loudness adaptation and examined a possible physiological basis for Loudness adaptation.
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End level bias on direct Loudness ratings of increasing sounds.
Journal of the Acoustical Society of America, 2010Co-Authors: Patrick Susini, Sabine Meunier, Régis Trapeau, Jacques ChatronAbstract:Three experiments on Loudness of sounds with linearly increasing levels were performed: global Loudness was measured using direct ratings, Loudness change was measured using direct and indirect estimations. Results revealed differences between direct and indirect estimations of Loudness change, indicating that the underlying perceptual phenomena are not the same. The effect of ramp size is small for the former and important for the latter. A similar trend was revealed between global Loudness and direct estimations of Loudness change according to the end level, suggesting they may have been confounded. Measures provided by direct estimations of Loudness change are more participant-dependent.
Birger Kollmeier - One of the best experts on this subject based on the ideXlab platform.
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Spectral and binaural Loudness summation for hearing-impaired listeners.
Hearing research, 2016Co-Authors: Dirk Oetting, Birger Kollmeier, Jens-e. Appell, Volker Hohmann, Stephan D. EwertAbstract:Sensorineural hearing loss typically results in a steepened Loudness function and a reduced dynamic range from elevated thresholds to uncomfortably loud levels for narrowband and broadband signals. Restoring narrowband Loudness perception for hearing-impaired (HI) listeners can lead to overly loud perception of broadband signals and it is unclear how binaural presentation affects Loudness perception in this case. Here, Loudness perception quantified by categorical Loudness scaling for nine normal-hearing (NH) and ten HI listeners was compared for signals with different bandwidth and different spectral shape in monaural and in binaural conditions. For the HI listeners, frequency- and level-dependent amplification was used to match the narrowband monaural Loudness functions of the NH listeners. The average Loudness functions for NH and HI listeners showed good agreement for monaural broadband signals. However, HI listeners showed substantially greater Loudness for binaural broadband signals than NH listeners: on average a 14.1 dB lower level was required to reach "very loud" (range 30.8 to -3.7 dB). Overall, with narrowband Loudness compensation, a given binaural Loudness for broadband signals above "medium loud" was reached at systematically lower levels for HI than for NH listeners. Such increased binaural Loudness summation was not found for Loudness categories below "medium loud" or for narrowband signals. Large individual variations in the increased Loudness summation were observed and could not be explained by the audiogram or the narrowband Loudness functions.
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Loudness of complex time-varying sounds ? A challenge for current Loudness models
2013Co-Authors: Jan Rennies, Jesko L Verhey, Jens-e. Appell, Birger KollmeierAbstract:The calculation of perceived Loudness is an important factor in many applications such as the assessment of noise emissions. Generally, Loudness of stationary sounds can be accurately predicted by existing models. For sounds with time-varying characteristics, however, there are still discrepancies between experimental data and model predictions, even with the most recent Loudness models. This contribution presents a series of experiments in which Loudness was measured in normal-hearing subjects with different types of realistic signals using an adaptive Loudness matching procedure and categorical Loudness scaling. The results of both methods indicate that Loudness of speech-like signals is largely determined by the long-term spectrum, while other speech-related properties (particularly temporal modulations) play only a minor role. Loudness of speech appears to be quite robust towards even severe signal modifications, as long as the long-term spectrum is similar. In contrast, Loudness of technical, strongly impulsive signals is considerably influenced by temporal modulations. For some of the signals, Loudness could not be predicted by current models. Since the perceived Loudness was underestimated by the models for some signals, but overestimated for other signals, a simple adjustment of the employed time constants in the temporal integration stage could not eliminate the discrepancies
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Modelling categorical Loudness perception for arbitrary listeners and sounds
The Journal of the Acoustical Society of America, 2008Co-Authors: Birger Kollmeier, Jesko L Verhey, Jens-e. Appell, Volker HohmannAbstract:While "classical" Loudness models predict Loudness in sone using the concepts of Stevens' compressive power law, (subdivided) categorical Loudness perception after Heller follows the compressive logarithmic Weber‐Fechner law. To bridge the gap between both approaches, this contribution reviews various steps towards a Loudness model that predicts categorical Loudness (in categorical units, CU) for normal and hearing‐impaired listeners for arbitrary sounds. It uses a (modified) classical Loudness model for stationary signals to derive the Loudness in sone and a nonlinear transformation from sone to CU. This transformation is approximated by a cubic polynomial equation wich is derived from categorical Loudness data of 84 normal‐hearing subjects. The model parameters are further set to predict the standard isophones that are in good agreement with the equal Loudness level contours derived from categorical Loudness data. Also, the model predicts the Loudness functions near threshold both for normal and hearing...
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Fine structure of hearing threshold and Loudness perception
The Journal of the Acoustical Society of America, 2004Co-Authors: Manfred Mauermann, Glenis R. Long, Birger KollmeierAbstract:Hearing thresholds measured with high-frequency resolution show a quasiperiodic change in level called threshold fine structure (or microstructure). The effect of this fine structure on Loudness perception over a range of stimulus levels was investigated in 12 subjects. Three different approaches were used. Individual hearing thresholds and equal Loudness contours were measured in eight subjects using Loudness-matching paradigms. In addition, the Loudness growth of sinusoids was observed at frequencies associated with individual minima or maxima in the hearing threshold from five subjects using a Loudness-matching paradigm. At low levels, Loudness growth depended on the position of the test- or reference-tone frequency within the threshold fine structure. The slope of Loudness growth differs by 0.2 dB/dB when an identical test tone is compared with two different reference tones, i.e., a difference in Loudness growth of 2 dB per 10-dB change in stimulus. Finally, Loudness growth was measured for the same f...
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Modeling Loudness growth and Loudness summation in hearing-impaired listeners
The Journal of the Acoustical Society of America, 1995Co-Authors: S. Launer, Volker Hohmann, Birger KollmeierAbstract:The goal of this study is to model the effect of sensorineural hearing impairment on Loudness perception for stationary stimuli of variable bandwidth. Loudness growth functions were obtained employing a categorical scaling technique with 10 categories. Loudness scaling was performed with 9 normal‐hearing and 14 sensorineural hearing‐impaired subjects employing bandfiltered noises with bandwidths between 1–6 critical bands. For normal‐hearing listeners, categorical scaling revealed similar differences across stimulus conditions as with Loudness balancing. The Loudness functions of the hearing‐impaired listeners show both, a steeper increase (recruitment) and reduced Loudness summation. Both aspects were successfully modeled by Zwicker’s Loudness model with three extensions to take account of hearing impairment. Raised audiometric threshold is modeled by a frequency‐dependent attenuation after calculation of excitation patterns. Increasing the exponent in calculating the specific Loudness yields recruitment...
Brian R Glasberg - One of the best experts on this subject based on the ideXlab platform.
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A revised model of Loudness perception applied to cochlear hearing loss.
Hearing research, 2004Co-Authors: Brian C J Moore, Brian R GlasbergAbstract:We previously described a model for Loudness perception for people with cochlear hearing loss. However, that model is incompatible with our most recent and most satisfactory model of Loudness for normal hearing. Here, we describe a Loudness model that is applicable to both normal and impaired hearing. In contrast to our earlier model for impaired hearing, the new model correctly predicts: (1) that a sound at absolute threshold has a small but finite Loudness; (2) that, for levels very close to the absolute threshold, the rate of growth of Loudness is similar for normal ears and ears with cochlear hearing loss; (3) the relation between monaural and binaural threshold and Loudness; (4) recent measures of equal-Loudness contours. Like the earlier model, the new model can account for the Loudness recruitment and reduced Loudness summation that are typically associated with cochlear hearing loss.
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a model of Loudness applicable to time varying sounds
Journal of The Audio Engineering Society, 2002Co-Authors: Brian R Glasberg, Brian C J MooreAbstract:Previously we described a model for calculating the Loudness of steady sounds from their spectrum. Here a new version of the model is presented, which uses a waveform as its input. The stages of the model are as follows. (a) A finite impulse response filter representing transfer through the outer and middle ear. (b) Calculation of the short-term spectrum using the fast Fourier transform (FFT). To give adequate spectral resolution at low frequencies, combined with adequate temporal resolution at high frequencies, six FFTs are calculated in parallel, using longer signal segments for low frequencies and shorter segments for higher frequencies. (c) Calculation of an excitation pattern from the physical spectrum. (d) Transformation of the excitation pattern to a specific Loudness pattern. (e) Determination of the area under the specific Loudness pattern. This gives a value for the instantaneous Loudness. The short-term perceived Loudness is calculated from the instantaneous Loudness using an averaging mechanism similar to an automatic gain control system, with attack and release times. Finally the overall Loudness impression is calculated from the short-term Loudness using a similar averaging mechanism, but with longer attack and release times. The new model gives very similar predictions to our earlier model for steady sounds. In addition, it can predict the Loudness of brief sounds as a function of duration and the overall Loudness of sounds that are amplitude modulated at various rates.
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a model for the prediction of thresholds Loudness and partial Loudness
Journal of The Audio Engineering Society, 1997Co-Authors: Brian C J Moore, Brian R Glasberg, Thomas BaerAbstract:A Loudness model for steady sounds is described having the following stages: 1) a fixed filter representing transfer through the outer ear; 2) a fixed filter representing transfer through the middle ear; 3) calculation of an excitation pattern from the physical spectrum; 4) transformation of the excitation pattern to a specific Loudness pattern; 5) determination of the area under the specific Loudness pattern, which gives overall Loudness for a given ear; and 6) summation of Loudness across ears. The model differs from earlier models in the following areas: 1) the assumed transfer function for the outer and middle ear; 2) the way that excitation patterns are calculated; 3) the way that specific Loudness is related to excitation for sounds in quiet and in noise; and (4) the way that binaural Loudness is calculated from monaural Loudness. The model is based on the assumption that sounds at absolute threshold have a small but finite Loudness. This Loudness is constant regardless of frequency and spectral content. It is also assumed that a sound at masked threshold has the same Loudness as a sound at absolute threshold. The model accounts well for recent measures of equal-Loudness contours, which differ from earlier measures because of improved control over bias effects. The model correctly predicts the relation between monaural and binaural threshold and Loudness. It also correctly accounts for the threshold and Loudness of complex sounds as a function of bandwidth.
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a revision of zwicker s Loudness model
Acustica, 1996Co-Authors: Brian C J Moore, Brian R GlasbergAbstract:Zwicker's Loudness model has the following stages: (a) A fixed filter representing transfer through the outer and middle ear; (b) Calculation of an excitation pattern from the physical spectrum; (c) Transformation of the excitation pattern to a specific Loudness pattern. The area under the specific Loudness pattern is assumed to determine Loudness. This paper presents some modifications and extensions to Zwicker's Loudness model. Changes are made in: (a) The assumed transfer function for the outer and middle ear; (b) The way that excitation patterns are calculated; (c) The way that specific Loudness is related to excitation for sounds in quiet and in noise. The revised model accounts more accurately than Zwicker's model for the way that equal-Loudness contours change with level. It also provides a more satisfactory explanation of why the Loudness of a sound of fixed intensity remains constant when the sound has a bandwidth less than the critical bandwidth (CB). Finally, the revised model is able to account for the Loudness of partially masked sounds without the introduction of correction factors. The revised model has the advantage that the excitation patterns on which it is based are calculated from analytical formulae rather than by reference to charts or tables. This avoids discontinuities in the predicted values of Loudness.
Colette M Mckay - One of the best experts on this subject based on the ideXlab platform.
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perception and prediction of Loudness in sound coding strategies using simultaneous electric stimulation
Hearing Research, 2020Co-Authors: Florian Langner, Colette M Mckay, Andreas Buchner, Waldo NogueiraAbstract:Abstract Cochlear Implant (CI) sound coding strategies based on simultaneous stimulation lead to an increased Loudness percept when compared to sequential stimulation using the same current levels. This is due to Loudness summation as a result of channel interactions. Studying the Loudness perception evoked by dual-channels compared to single-channels can be useful to optimize sound coding strategies that use simultaneous current pulses. Fourteen users of HiRes90k implants and one user of a CII implant Loudness balanced single-channel to dual-channel stimuli with varying distance between simultaneous channels. In this study each component of a dual channel was a virtual channel, which shared current across two adjacent electrodes. Balancing was performed at threshold and comfortable level, for two spatial references (apical and basal) and for dual-channels with different relative current ratios. Increasing distance between dual-channels decreased the amount of current compensation in the dual-channel required to reach equal Loudness to a single channel component by an average of 0.24 dB / mm without a significant difference between threshold and most comfortable level. If the components of the dual-channels were not at equal Loudness, the Loudness summation was reduced with respect to the equal Loudness case. The results were incorporated into an existing Loudness model by McKay et al. (2003). The predictions from the adapted model were evaluated by comparing the Loudness evoked by simultaneous and sequential sound coding strategies. The application of the adapted model resulted in a deviation between predicted and actual behavioral Loudness balancing adjustments in electrical level between simultaneous and sequential processing strategies of 0.24 dB on average.
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Loudness summation for pulsatile electrical stimulation of the cochlea effects of rate electrode separation level and mode of stimulation
Journal of the Acoustical Society of America, 2001Co-Authors: Colette M Mckay, Maria D Remine, Hugh J McdermottAbstract:The aim of these two experiments was to gain systematic data on the amount of Loudness summation measured for dual-electrode stimuli with varying temporal and spatial separation of current pulses. Loudness summation is important in the implementation of speech processing strategies for implantees. However, the Loudness mapping functions used in current speech processors utilize psychophysical data (thresholds and comfortable Loudness levels) derived using single-electrode stimuli, and do not take into account the temporal and spatial patterns of the speech processor output. In the first experiment, the current reduction required to equalize the Loudness of a dual-electrode stimulus to that of its component (and equally loud) single-electrode stimuli was measured for three electrode separations (0.75, 2.25, and 7.5 mm), three repetition rates (250, 500, and 1000 Hz), and two Loudness levels (comfortably loud, and mid-dynamic range). It was found that electrode separation had little effect on Loudness summation, except for interactions with level and rate effects at the smallest separation. More current adjustment (in dB) was required for higher rates and lower levels of stimulation. The second experiment investigated the effects of mode (monopolar versus bipolar) and pulse duration on Loudness summation. More current adjustment was required in bipolar mode than in monopolar mode at the lower level only. The main effects in both experiments, and their interactions, are consistent with a Loudness model in which the neural excitation density is first obtained by temporal integration of excitation at each cochlear place, then converted to specific Loudness via a nonlinear relationship, and finally integrated over cochlear place to obtain the Loudness. The two important features which affect the Loudness relationships in dual-electrode stimulation in this model are the shape of the excitation density function and the amount by which the neural spike probability per pulse is reduced in areas of overlapping excitation due to refractory effects.
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Loudness summation for pulsatile electrical stimulation of the cochlea effects of rate electrode separation level and mode of stimulation
Journal of the Acoustical Society of America, 2001Co-Authors: Colette M Mckay, Maria D Remine, Hugh J McdermottAbstract:The aim of these two experiments was to gain systematic data on the amount of Loudness summation measured for dual-electrode stimuli with varying temporal and spatial separation of current pulses. Loudness summation is important in the implementation of speech processing strategies for implantees. However, the Loudness mapping functions used in current speech processors utilize psychophysical data (thresholds and comfortable Loudness levels) derived using single-electrode stimuli, and do not take into account the temporal and spatial patterns of the speech processor output. In the first experiment, the current reduction required to equalize the Loudness of a dual-electrode stimulus to that of its component (and equally loud) single-electrode stimuli was measured for three electrode separations (0.75, 2.25, and 7.5 mm), three repetition rates (250, 500, and 1000 Hz), and two Loudness levels (comfortably loud, and mid-dynamic range). It was found that electrode separation had little effect on Loudness summa...
