Pitch perception as probabilistic inference

Pitch is a fundamental and salient perceptual attribute of many behaviourally important sounds, including animal calls, human speech and music. Human listeners perceive pitch without conscious effort or attention. These and similar observations have prompted a search for mappings from acoustic stimulus to percept that can be easily computed from peripheral neural responses at early stages of the central auditory pathway. This tenet however is not supported by physiological evidence: how the percept of pitch is encoded in neural firing patterns across the brain, and where – if at all – such a representation may be localised remain as yet unsolved questions. Here, instead of seeking an explanation guided by putative mechanisms, we take a more abstract stance in developing a model by asking, what computational goal the auditory system is set up to achieve during pitch perception. Many natural pitch-evoking sounds are approximately periodic within short observation time windows. We posit that pitch reflects a near-optimal estimate of the underlying periodicity of sounds from noisy evoked responses in the auditory nerve, exploiting statistical knowledge about the regularities and irregularities occurring during sound generation and transduction. We compute (or approximate) the statistically optimal estimate using a Bayesian probabilistic framework. Model predictions match the pitch reported by human listeners for a wide range of welldocumented, pitch-evoking stimuli, both periodic and aperiodic. We then present new psychophysical data on octave biases and pitch-timbre interactions in human perception which further demonstrates the validity of our approach, while posing difficulties for alternative models based on autocorrelation analysis or simple spectral pattern matching. Our model embodies the concept of perception as unconscious inference, originally proposed by von Helmholtz as an interface bridging optics and vision. Our results support the view that even apparently primitive acoustic percepts may derive from subtle statistical inference, suggesting that such inferential processes operate at all levels across our sensory systems

Preliminary Results on the Use of an Antiserum to Human Parathyrin in a Homologous Radioimmunoassay

Peer Reviewe

Personalizing transient noise reduction algorithm settings for cochlear implant users

Objectives: Speech understanding in noise is difficult for patients with a cochlear implant. One common and disruptive type of noise is transient noise. We have tested transient noise reduction (TNR) algorithms in cochlear implant users to investigate the merits of personalizing the noise reduction settings based on a subject's own preference. Design: The effect of personalizing two parameters of a broadband and a multiband TNR algorithm (TNRbb and TNRmb, respectively) on speech recognition was tested in a group of 15 unilaterally implanted subjects in cafeteria noise. The noise consisted of a combination of clattering dishes and babble noise. Each participant could individually vary two parameters, namely the scaling factor of the attenuation and the release time (tau). The parameter tau represents the duration of the attenuation applied after a transient is detected. As a reference, the current clinical standard TNR "SoundRelax" from Advanced Bionics was tested (TNRbb-std). Effectiveness of the algorithms on speech recognition was evaluated adaptively by determining the speech reception threshold (SRT). Possible subjective benefits of the algorithms were assessed using a rating task at a fixed signal-to-noise ratio (SNR) of SRT + 3 dB. Rating was performed on four items, namely speech intelligibility, speech naturalness, listening effort, and annoyance of the noise. Word correct scores were determined at these fixed speech levels as well. Results: The personalized TNRmb improved the SRT statistically significantly with 1.3 dB, while the personalized TNRbb degraded it significantly by 1.7 dB. For TNRmb, we attempted to further optimize its settings by determining a group-based setting, leaving out those subjects that did not experience a benefit from it. Using these group-based settings, however, TNRmb did not have a significant effect on the SRT any longer. TNRbb-std did not affect speech recognition significantly. No significant effects on subjective ratings were found for any of the items investigated. In addition, at a constant speech level of SRT + 3 dB, no effect of any of the algorithms was found on word correct scores, including TNRmb with personalized settings. Conclusions: Our study results indicate that personalizing noise reduction settings of a multiband TNR algorithm can significantly improve speech intelligibility in transient noise, but only under challenging listening conditions around the SRT. At more favorable SNRs (SRT + 3 dB), this benefit was lost. We hypothesize that TNRmb was beneficial at lower SNRs, because of more effective artifact detection under those conditions. Group-averaged settings of the multiband algorithm did not significantly affect speech recognition. TNRbb decreased speech recognition significantly using personalized parameter settings. Rating scores were not significantly affected by the algorithms under any condition tested. The currently available TNR algorithm for Advanced Bionics systems (SoundRelax) is a broadband filter that does not support personalization of its settings. Future iterations of this algorithm might benefit from upgrading it to a multiband variant with the option to personalize its parameter settings.Disorders of the head and nec