Phase effects on the masking of speech by harmonic complexes: Variations with level

Speech reception thresholds were obtained in normally hearing listeners for sentence targets masked by harmonic complexes constructed with different phase relationships. Maskers had either a constant fundamental frequency (F0), or had F0 changing over time, following a pitch contour extracted from natural speech. The median F0 of the target speech was very similar to that of the maskers. In experiment 1 differences in the masking produced by Schroeder positive and Schroeder negative phase complexes were small (around 1.5 dB) for moderate levels [60 dB sound pressure level (SPL)], but increased to around 6 dB for maskers at 80 dB SPL. Phase effects were typically around 1.5 dB larger for maskers that had naturally varying F0 contours than for maskers with constant F0. Experiment 2 showed that shaping the long-term spectrum of the maskers to match the target speech had no effect. Experiment 3 included additional phase relationships at moderate levels and found no effect of phase. Therefore, the phase relationship within harmonic complexes appears to have only minor effects on masking effectiveness, at least at moderate levels, and when targets and maskers are in the same F0 range

Spike-Timing-Based Computation in Sound Localization

Spike timing is precise in the auditory system and it has been argued that it conveys information about auditory stimuli, in particular about the location of a sound source. However, beyond simple time differences, the way in which neurons might extract this information is unclear and the potential computational advantages are unknown. The computational difficulty of this task for an animal is to locate the source of an unexpected sound from two monaural signals that are highly dependent on the unknown source signal. In neuron models consisting of spectro-temporal filtering and spiking nonlinearity, we found that the binaural structure induced by spatialized sounds is mapped to synchrony patterns that depend on source location rather than on source signal. Location-specific synchrony patterns would then result in the activation of location-specific assemblies of postsynaptic neurons. We designed a spiking neuron model which exploited this principle to locate a variety of sound sources in a virtual acoustic environment using measured human head-related transfer functions. The model was able to accurately estimate the location of previously unknown sounds in both azimuth and elevation (including front/back discrimination) in a known acoustic environment. We found that multiple representations of different acoustic environments could coexist as sets of overlapping neural assemblies which could be associated with spatial locations by Hebbian learning. The model demonstrates the computational relevance of relative spike timing to extract spatial information about sources independently of the source signal

A phenomenological model of the synapse between the inner hair cell and auditory nerve: Long-term adaptation with power-law dynamics

There is growing evidence that the dynamics of biological systems that appear to be exponential over short time courses are in some cases better described over the long-term by power-law dynamics. A model of rate adaptation at the synapse between inner hair cells and auditory-nerve (AN) fibers that includes both exponential and power-law dynamics is presented here. Exponentially adapting components with rapid and short-term time constants, which are mainly responsible for shaping onset responses, are followed by two parallel paths with power-law adaptation that provide slowly and rapidly adapting responses. The slowly adapting power-law component significantly improves predictions of the recovery of the AN response after stimulus offset. The faster power-law adaptation is necessary to account for the “additivity” of rate in response to stimuli with amplitude increments. The proposed model is capable of accurately predicting several sets of AN data, including amplitude-modulation transfer functions, long-term adaptation, forward masking, and adaptation to increments and decrements in the amplitude of an ongoing stimulus