scholarly journals Evaluation of an Adaptive Dynamic Compensation System in Cochlear Implant Listeners

2020 ◽  
Vol 24 ◽  
pp. 233121652097034
Author(s):  
Florian Langner ◽  
Andreas Büchner ◽  
Waldo Nogueira
Keyword(s):  
Signal Processing ◽  
Cochlear Implant ◽  
Speech Intelligibility ◽  
Discrimination Task ◽  
Signal To Noise Ratio ◽  
Compensation System ◽  
Signal To Noise ◽  
Dynamic Compensation ◽  
Front End ◽  
Noise Ratio

Cochlear implant (CI) sound processing typically uses a front-end automatic gain control (AGC), reducing the acoustic dynamic range (DR) to control the output level and protect the signal processing against large amplitude changes. It can also introduce distortions into the signal and does not allow a direct mapping between acoustic input and electric output. For speech in noise, a reduction in DR can result in lower speech intelligibility due to compressed modulations of speech. This study proposes to implement a CI signal processing scheme consisting of a full acoustic DR with adaptive properties to improve the signal-to-noise ratio and overall speech intelligibility. Measurements based on the Short-Time Objective Intelligibility measure and an electrodogram analysis, as well as behavioral tests in up to 10 CI users, were used to compare performance with a single-channel, dual-loop, front-end AGC and with an adaptive back-end multiband dynamic compensation system (Voice Guard [VG]). Speech intelligibility in quiet and at a +10 dB signal-to-noise ratio was assessed with the Hochmair–Schulz–Moser sentence test. A logatome discrimination task with different consonants was performed in quiet. Speech intelligibility was significantly higher in quiet for VG than for AGC, but intelligibility was similar in noise. Participants obtained significantly better scores with VG than AGC in the logatome discrimination task. The objective measurements predicted significantly better performance estimates for VG. Overall, a dynamic compensation system can outperform a single-stage compression (AGC + linear compression) for speech perception in quiet.

Parallel Programming and Its Architectures Based on Data Access Separated Algorithm Kernels

2012 ◽  
pp. 278-296
Author(s):  
Dake Liu ◽  
Joar Sohl ◽  
Jian Wang
Keyword(s):  
Signal Processing ◽  
Parallel Programming ◽  
High Performance ◽  
Data Access ◽  
Time Signal ◽  
Hardware System ◽  
Memory Subsystem ◽  
Computing Performance ◽  
Processing Platform ◽  
Parallel Signal Processing

A novel master-multi-SIMD architecture and its kernel (template) based parallel programming flow is introduced as a parallel signal processing platform. The name of the platform is ePUMA (embedded Parallel DSP processor architecture with Unique Memory Access). The essential technology is to separate data accessing kernels from arithmetic computing kernels so that the run-time cost of data access can be minimized by running it in parallel with algorithm computing. The SIMD memory subsystem architecture based on the proposed flow dramatically improves the total computing performance. The hardware system and programming flow introduced in this article will primarily aim at low-power high-performance embedded parallel computing with low silicon cost for communications and similar real-time signal processing.

Research on False Points Removing of Speech Segmentation

2014 ◽  
Vol 536-537 ◽  
pp. 136-140
Author(s):  
Chong Wang ◽  
Jun Feng Zhao ◽  
Rong Huang
Keyword(s):  
Signal Processing ◽  
Speech Signal ◽  
Wavelet Transformation ◽  
Low Frequency ◽  
Frequency Component ◽  
Speech Segmentation ◽  
Speech Signal Processing ◽  
Front End ◽  
Cumulate Energy

In speech signal processing, the techniques of speech segmentation as front end of preprocessing have great importance in speech enhancing, coding and recognition. This paper analyzes the performances of several typical algorithms of speech segmentation, which are compared with each other. It put emphasis on the study of the algorithm based on the wavelet transformation. The smooth and gradual changing low frequency component can not segment the speech efficiently. In order to solve the problem, this paper put forward to an algorithm based on the cumulate energy of the wavelet transformation which promotes the precision of the segmentation on the phoneme level. But as a result of the wavelet sensitivity, it will present certain number of false spots. Therefore this paper proposes tow methods removing false spots. Finally it makes certain summary to these technologies.

Simulation of A/D Conversion with LabView

2013 ◽  
Vol 706-708 ◽  
pp. 1907-1910
Author(s):  
Bing Wang ◽  
Xiao Li Wang
Keyword(s):  
Signal Processing ◽  
Model Simulation ◽  
Signal Process ◽  
Dual Channel ◽  
Simulink Model ◽  
Analog Signals ◽  
Simultaneous Acquisition ◽  
Parallel Environment ◽  
And Performance ◽  
Parallel Signal Processing

Simulation technology was a very important technology in the parallel signal processing research. A type of antomatic controlling simulation system was introduced, which was built by taking LabVIEW as the software platform, using LabVIEW user interface interacting with the Simulink model. Simulation of processing often requires algorithm and performance evaluation of parallel environment. Signal processing often requires simultaneous acquisition of multi-channel analog signals. Taking dual-channel A/D conversion processes as an example, using LabVIEW with VC, it achieves a simulation of parallel signal process of A/D conversion process.

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