Psychophysical measures of tonotopic selectivity in cats and humans

Sound spectra are represented by patterns of activity along the tonotopic axis ofthe cochlea. Cochlear implants can transmit spectra by stimulating tonotopicallyappropriate electrodes, but fidelity is limited by intracochlear spread of excitation. We aim to better evaluate present-day experimental stimulation procedures and, potentially, to improve transmission of spectra with novel stimulation modalities. As a first step, we are developing non-invasive measures of tonotopic spread of excitation that can be compared between normal-hearing cats and humans. These measures include psychophysics in the present study and scalp-recorded electrophysiology in a companion study (Guérit et al., 2021). Cats and humans detected pure-tone probes presented in continuous 1/8- and 1-oct noise-band maskers. Masker bandwidths were readily discernable in both species by the dependence of masked thresholds on probe frequencies. Thresholds were largely constant across the bandwidth of the 1-oct masker, whereas thresholds dropped markedly at frequencies away from the center of the 1/8-oct masker. Cats and humans differed in that the feline auditory filter centered on 8 kHz, which we measured using a notched-noise procedure, was 22% wider than published values for humans at the same center frequency. Also, thresholds for the cats in the 1-octmasker condition consistently were 1.0 to 3.2 dB higher than expected based on the estimated masker power in the feline auditory filter. The present psychophysical results parallel those in our companion electrophysiological study, thereby providing perceptual validation for that study. These psychophysical and electrophysiological methods will be valuable for future investigations of novel approaches for auditory prosthesis.

Application of Bayesian Active Learning to the Estimation of Auditory Filter Shapes Using the Notched-Noise Method

Time-efficient hearing tests are important in both clinical practice and research studies. This particularly applies to notched-noise tests, which are rarely done in clinical practice because of the time required. Auditory-filter shapes derived from notched-noise data may be useful for diagnosis of the cause of hearing loss and for fitting of hearing aids, especially if measured over a wide range of center frequencies. To reduce the testing time, we applied Bayesian active learning (BAL) to the notched-noise test, picking the most informative stimulus parameters for each trial based on nine Gaussian Processes. A total of 11 hearing-impaired subjects were tested. In 20 to 30 min, the test provided estimates of signal threshold as a continuous function of frequency from 500 to 4000 Hz for nine notch widths and for notches placed both symmetrically and asymmetrically around the signal frequency. The thresholds were found to be consistent with those obtained using a 2-up/1-down forced-choice procedure at a single center frequency. In particular, differences in threshold between the methods did not vary with notch width. An independent second run of the BAL test for one notch width showed that it is reliable. The data derived from the BAL test were used to estimate auditory-filter width and asymmetry and detection efficiency for center frequencies from 500 to 4000 Hz. The results agreed with expectations for cochlear hearing losses that were derived from the audiogram and a hearing model.

No Effect of Musical Training on Frequency Selectivity Estimated Using Three Methods

It is widely believed that the frequency selectivity of the auditory system is largely determined by processes occurring in the cochlea. If so, musical training would not be expected to influence frequency selectivity. Consistent with this, auditory filter shapes for low center frequencies do not differ for musicians and nonmusicians. However, it has been reported that psychophysical tuning curves (PTCs) at 4000 Hz were sharper for musicians than for nonmusicians. This study explored the origin of the discrepancy across studies. Frequency selectivity was estimated for musicians and nonmusicians using three methods: fast PTCs with a masker that swept in frequency, “traditional” PTCs obtained using several fixed masker center frequencies, and the notched-noise method. The signal frequency was 4000 Hz. The data were fitted assuming that each side of the auditory filter had the shape of a rounded-exponential function. The sharpness of the auditory filters, estimated as the Q10 values, did not differ significantly between musicians and nonmusicians for any of the methods, but detection efficiency tended to be higher for the musicians. This is consistent with the idea that musicianship influences auditory proficiency but does not influence the peripheral processes that determine the frequency selectivity of the auditory system.

Auditory Inspired Convolutional Neural Networks for Ship Type Classification with Raw Hydrophone Data

Detecting and classifying ships based on radiated noise provide practical guidelines for the reduction of underwater noise footprint of shipping. In this paper, the detection and classification are implemented by auditory inspired convolutional neural networks trained from raw underwater acoustic signal. The proposed model includes three parts. The first part is performed by a multi-scale 1D time convolutional layer initialized by auditory filter banks. Signals are decomposed into frequency components by convolution operation. In the second part, the decomposed signals are converted into frequency domain by permute layer and energy pooling layer to form frequency distribution in auditory cortex. Then, 2D frequency convolutional layers are applied to discover spectro-temporal patterns, as well as preserve locality and reduce spectral variations in ship noise. In the third part, the whole model is optimized with an objective function of classification to obtain appropriate auditory filters and feature representations that are correlative with ship categories. The optimization reflects the plasticity of auditory system. Experiments on five ship types and background noise show that the proposed approach achieved an overall classification accuracy of 79.2%, which improved by 6% compared to conventional approaches. Auditory filter banks were adaptive in shape to improve accuracy of classification.