Spontaneous otocoherence of the active ear
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Spontaneous otoacoustic emission (SOAE) provides compelling evidence of active force generation inside the inner ear, although there is significant debate about the underlying generation mechanism(s). SOAE is commonly characterized by peaks in a spectral domain representation (as derived from a discrete Fourier transform), occurring at idiosyncratic frequencies unique to a given ear. Such is typically computed as an averaged magnitude spectrum that discards phase information. Here, we explore the hypothesis that SOAE phase, readily extracted from pre-existing recordings, contains complementary information. We propose several measures to use this information to quantify otocoherence (a form of autocoherence referring to a phenomenon of the ear), primarily by measuring the consistency in SOAE phase accumulation over a particular timescale. We present results based on recordings from different species with disparate inner ear morphologies (humans, barn owls, lizards). For regions of SOAE activity we extract time constants representing the timescale over which otocoherence is maintained. We demonstrate that these vary significantly across species and (for the barn owl and Tokay gecko where this data is available) appear to correlate with measures of auditory nerve fiber tuning. Additionally, we adapted the method to identify regions of weak SOAE activity among fluctuations in the noise floor. These methods can readily be employed to re-analyze SOAE waveforms previously collected from a variety of species, making them of broader comparative utility to reveal information about SOAE generation and thereby active cochlear mechanics.
Significance Statement
We examined spontaneous otoacoustic emission, a widely accepted hallmark of active auditory biomechanics, and describe a novel approach to extract measures of its self-coherence. This was explored comparatively across different species of disparate inner ear morphology to provide insight into how active amplification operates in the presence of noise. Our approach elucidates the notion that what has been traditionally referred to as “spontaneous activity” is not unstructured noise, but fluctuations that reveal internal system dynamics. The methodology described here is straightforward to employ and can be applied to a variety of data types, both within and beyond auditory neuroscience.
