Two stages of bandwidth scaling drives efficient neural coding of natural sounds

Coupling-induced tunability of characteristic frequency, bandwidth and gain of artificial hair cells

Drawing inspiration from nature, we develop bio-inspired acoustic sensors with integrated signal processing capabilities to (i) close the performance gap between the human hearing and machine hearing and (ii) test models on biological hearing. Particularly important is thereby the combination of frequency decomposition with nonlinear (compressive) amplification of the sound signals. Here, the question arises, how the frequency resolution of 0.1-0.4%, the large gain and the coverage of the large frequency range of 20 Hz to 20 kHz can be obtained with a modest number of 3000 inner hair cells as transducers without missing tones. To solve this issue, it was hypothesized that the cochlea can be modeled as coupled critical oscillators. We study experimentally and theoretically the effects of coupling critical oscillators using bio-inspired acoustic sensors, which are based-on microelectromechanical system (MEMS) resonators with a high-quality factor and a resonance frequency set by the geometry. Using electronic feedback, these resonators act like critical oscillators tuned near Andronov-Hopf bifurcation point. If output-signal coupling is added, three different bifurcation points are generated. Tuning the system close to one of these bifurcation points leads to a highly tunable behavior and sound pressure dependent sensitivity that is compressive in nature. In this case, the response frequency of the sensor system can be shifted by tuning the control parameter for bifurcation, allowing to cover larger bandwidths with one sensor pair while retaining high quality factors. Furthermore, tuning coupling and feedback strength, bandwidth and gain of each sensor can be adapted as needed. Using these effects, an adaptive filter bank to model the cochlear functionality and adaptation can be build. Since efferent feedback can tune the response of outer hair cells and thus inner hair cells and basilar membrane as well, the question arises if such tuning mechanisms can be observed in the mammalian cochlea as well.

Interference of mid-level sound statistics underlie human speech recognition sensitivity in natural noise

Recognizing speech in noise, such as in a busy restaurant, is an essential cognitive skill where the task difficulty varies across environments and noise levels. Although there is growing evidence that the auditory system relies on statistical representations for perceiving 1-5 and coding4,6-9 natural sounds, it's less clear how statistical cues and neural representations contribute to segregating speech in natural auditory scenes. We demonstrate that human listeners rely on mid-level statistics to segregate and recognize speech in environmental noise. Using natural backgrounds and variants with perturbed spectro-temporal statistics, we show that speech recognition accuracy at a fixed noise level varies extensively across natural backgrounds (0% to 100%). Furthermore, for each background the unique interference created by summary statistics can mask or unmask speech, thus hindering or improving speech recognition. To identify the neural coding strategy and statistical cues that influence accuracy, we developed generalized perceptual regression, a framework that links summary statistics from a neural model to word recognition accuracy. Whereas a peripheral cochlear model accounts for only 60% of perceptual variance, summary statistics from a mid-level auditory midbrain model accurately predicts single trial sensory judgments, accounting for more than 90% of the perceptual variance. Furthermore, perceptual weights from the regression framework identify which statistics and tuned neural filters are influential and how they impact recognition. Thus, perception of speech in natural backgrounds relies on a mid-level auditory representation involving interference of multiple summary statistics that impact recognition beneficially or detrimentally across natural background sounds.

Human Auditory Ecology: Extending Hearing Research to the Perception of Natural Soundscapes by Humans in Rapidly Changing Environments

Research in hearing sciences has provided extensive knowledge about how the human auditory system processes speech and assists communication. In contrast, little is known about how this system processes "natural soundscapes," that is the complex arrangements of biological and geophysical sounds shaped by sound propagation through non-anthropogenic habitats [Grinfeder et al. (2022). Frontiers in Ecology and Evolution. 10: 894232]. This is surprising given that, for many species, the capacity to process natural soundscapes determines survival and reproduction through the ability to represent and monitor the immediate environment. Here we propose a framework to encourage research programmes in the field of "human auditory ecology," focusing on the study of human auditory perception of ecological processes at work in natural habitats. Based on large acoustic databases with high ecological validity, these programmes should investigate the extent to which this presumably ancestral monitoring function of the human auditory system is adapted to specific information conveyed by natural soundscapes, whether it operate throughout the life span or whether it emerges through individual learning or cultural transmission. Beyond fundamental knowledge of human hearing, these programmes should yield a better understanding of how normal-hearing and hearing-impaired listeners monitor rural and city green and blue spaces and benefit from them, and whether rehabilitation devices (hearing aids and cochlear implants) restore natural soundscape perception and emotional responses back to normal. Importantly, they should also reveal whether and how humans hear the rapid changes in the environment brought about by human activity.