A Loudness Model for Time-Varying Sounds Incorporating Binaural Inhibition

Abstract

This article describes a model of loudness for time-varying sounds that incorporates the concept of binaural inhibition, namely, that the signal applied to one ear can reduce the internal response to a signal at the other ear. For each ear, the model includes the following: a filter to allow for the effects of transfer of sound through the outer and middle ear; a short-term spectral analysis with greater frequency resolution at low than at high frequencies; calculation of an excitation pattern, representing the magnitudes of the outputs of the auditory filters as a function of center frequency; application of a compressive nonlinearity to the output of each auditory filter; and smoothing over time of the resulting instantaneous specific loudness pattern using an averaging process resembling an automatic gain control. The resulting short-term specific loudness patterns are used to calculate broadly tuned binaural inhibition functions, the amount of inhibition depending on the relative short-term specific loudness at the two ears. The inhibited specific loudness patterns are summed across frequency to give an estimate of the short-term loudness for each ear. The overall short-term loudness is calculated as the sum of the short-term loudness values for the two ears. The long-term loudness for each ear is calculated by smoothing the short-term loudness for that ear, again by a process resembling automatic gain control, and the overall loudness impression is obtained by summing the long-term loudness across ears. The predictions of the model are more accurate than those of an earlier model that did not incorporate binaural inhibition.

Tonotopic representation of loudness in the human cortex

“…The weak significant entrainment to channel-specific short-term loudness may reflect a genuine effect or it may reflect false positives, since the channel-specific short-term loudness is correlated with the channel-specific instantaneous loudness for the same channel ( ρ  = 0.9, 0.8, 0.8, 0.8, 0.8, 0.8, 0.7, 0.8 and 0.7 for channels 1–9 respectively), and, for the middle channels, the channel-specific short-term loudness is correlated with the overall short-term loudness ( ρ  = 0.9, 0.8 and 0.8 for channels 3–5 respectively). Hence the data do not allow a clear conclusion about whether channel-specific instantaneous loudness is summed across channels to give the overall instantaneous loudness and the overall short-term loudness is determined from the overall instantaneous loudness, as assumed in the loudness model of Glasberg and Moore (2002), or whether short-term loudness is determined separately for each channel from the instantaneous loudness for that channel, and then short-term loudness values are summed across channels to give the overall short-term loudness, as assumed in the models of Chalupper and Fastl (2002) and Moore et al. (2016).…”

“…1) is not the only plausible sequence leading to the neural computation of overall short-term loudness. Short-term loudness may instead be derived separately for each channel, and these short-term loudness estimates may then be combined across channels to give the overall short-term loudness, as assumed in the models of Chalupper and Fastl (2002) and Moore et al. (2016).…”

Binaural summation of amplitude modulation involves weak interaural suppression

“…1a. For example, binaural inhibition is a common feature 55 , often occurring across multiple timescales 53 . However these models are typically designed with a focus on explaining perception across a range of frequencies (and for inputs of arbitrary frequency content), rather than attempting to understand performance on specific tasks (i.e.…”

Auditory detection is modulated by theta phase of silent lip movements

“…The same noise consisted in a Gaussian white noise lasting two seconds for the calibration task and five seconds in the tone detection task (the white noise has been generated only once and loaded during each procedure to ensure that all the participants were tested with the same noise; sampling rate: 44100 Hz). Both the tone and the white noise signals were normalized between -1 to 1 (arbitrary units) and the loudness of the resultant stimuli (white noise with embedded tone) was estimated at ~ 91 dB SPL sound pressure level (SPL) using inhouse Matlab codes (Moore et al, 2016). During the entire procedure, the auditory stimuli were displayed at constant ~72 dB SPL and across participants.…”