**Cochlear Implants**

### **Temporal Filterbanks in Cochlear Implant Hearing and Deep Learning Simulations Temporal Filterbanks in Cochlear Implant Hearing and Deep Learning Simulations**

## Payton Lin Payton Lin

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http://dx.doi.org/10.5772/66627

#### **Abstract**

The masking phenomenon has been used to investigate cochlear excitation patterns and has even motivated audio coding formats for compression and speech processing. For example, cochlear implants rely on masking estimates to filter incoming sound signals onto an array. Historically, the critical band theory has been the mainstay of psychoacoustic theory. However, masked threshold shifts in cochlear implant users show a discrepancy between the observed critical bandwidths, suggesting separate roles for place location and temporal firing patterns. In this chapter, we will compare discrimination tasks in the spectral domain (e.g., power spectrum models) and the temporal domain (e.g., temporal envelope) to introduce new concepts such as profile analysis, temporal critical bands, and transition bandwidths. These recent findings violate the fundamental assumptions of the critical band theory and could explain why the masking curves of cochlear implant users display spatial and temporal characteristics that are quite unlike that of acoustic stimulation. To provide further insight, we also describe a novel analytic tool based on deep neural networks. This deep learning system can simulate many aspects of the auditory system, and will be used to compute the efficiency of spectral filterbanks (referred to as "FBANK") and temporal filterbanks (referred to as "TBANK").

**Keywords:** auditory masking, cochlear implants, filter bandwidths, filterbanks, deep neural networks, deep learning, machine learning, compression, audio coding, speech pattern recognition, profile analysis, temporal critical bands, transition bandwidths

## **1. Introduction**

The transformation of sound into a representation within the auditory system involves many layers of information analysis and processing. Sound is first converted into nervous impulses by cochlear hair cells, which are mechanically organized to distribute the spectral energy of

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© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, © 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

their excitation along the length of the basilar membrane (**Figure 1**). The connecting nerve fibers show a bandpass response to the input signal, where the density of firings for a particular fiber varies with the stimulus intensity over a certain range. Basic information from a sound is then extracted and passed to subsequent stages for perceptual machinery to present its own construction of reality. Noninvasive methods are needed to investigate the influence of these higher-level perceptual processes on the properties of the cochlea. For example, the method of psychophysical inference is often used to fill in the gaps of physiological knowledge.

**Figure 1.** Spatial arrangement of cochlear hair cells along the basilar membrane (base to apex).

This chapter will be divided into two sections. In the section on human hearing research, we will predict neural firing characteristics from the perspective of psychoacoustic "*masking*" experiments. In the section on machine hearing research, we will compare artificial neural network input from the perspective of machine learning experiments. Experimental data is presented in both human hearing and machine hearing to supplement the incompleteness of current neurophysical methods by providing new insight into the stages of processing.
