An integrated model of pitch perception incorporating place and temporal pitch codes with application to cochlear implant research.

Although the neural mechanisms underlying pitch perception are not yet fully understood, there is general agreement that place and temporal representations of pitch are both used by the auditory system. This paper describes a neural network model of pitch perception that integrates both codes of pitch and explores the contributions of, and the interactions between, the two representations in simulated pitch ranking trials in normal and cochlear implant hearing. The model can replicate various psychophysical observations including the perception of the missing fundamental pitch and sensitivity to pitch interval sizes. As a case study, the model was used to investigate the efficiency of pitch perception cues in a novel sound processing scheme, Stimulation based on Auditory Modelling (SAM), that aims to improve pitch perception in cochlear implant hearing. Results showed that enhancement of the pitch perception cues would lead to better pitch ranking scores in the integrated model only if the place and temporal pitch cues were consistent.

Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks.

Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons' action potentials (spikes) as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy.

Application of a pitch perception model to investigate the effect of stimulation field spread on the pitch ranking abilities of cochlear implant recipients.

Although many cochlear implant (CI) recipients perceive speech very well in favorable conditions, they still have difficulty with music, speech in noisy environments, and tonal languages. Studies show that CI users' performance in these tasks are correlated with their ability to perceive pitch. The spread of stimulation field from the electrodes to the auditory nerve is one of the factors affecting performance. This study proposes a model of auditory perception to predict the performance of CI users in pitch ranking tasks using an existing sound processing scheme. The model is then used as a platform to investigate the effect of stimulation field spread on performance.

Wavelet adaptation for automatic voice disorders sorting.

Early diagnosis of voice disorders and abnormalities by means of digital speech processing is a subject of interest for many researchers. Various methods are introduced in the literature, some of which are able to extensively discriminate pathological voices from normal ones. Voice disorders sorting, on the other hand, has received less attention due to the complexity of the problem. Although, previous publications show satisfactory results in classifying one type of disordered voice from normal cases, or two different types of abnormalities from each other, no comprehensive approach for automatic sorting of vocal abnormalities has been offered yet. In this paper, a solution for this problem is suggested. We create a powerful wavelet feature extraction approach, in which, instead of standard wavelets, adaptive wavelets are generated and applied to the voice signals. Orthogonal wavelets are parameterized via lattice structure and then, the optimal parameters are investigated through an iterative process, using the genetic algorithm (GA). GA is guided by the classifier results. Based on the generated wavelet, a wavelet-filterbank is constructed and the voice signals are decomposed to compute eight energy-based features. A support vector machine (SVM) then classifies the signals using the extracted features. Experimental results show that six various types of vocal disorders: paralysis, nodules, polyps, edema, spasmodic dysphonia and keratosis are fully sorted via the proposed method. This could be a successful step toward sorting a larger number of abnormalities associated with the vocal system.