This paper presents the real-time implementation of an adaptive speech processing pipeline for cochlear implants on the smartphone platform. The pipeline is capable of real-time classification of background noise environment and automated tuning of a noise suppression component based upon the detected background noise environment. This pipeline was previously implemented on the FDA-approved PDA platform for cochlear implant studies. The paper discusses the steps taken to achieve the real-time implementation of the pipeline on the smartphone platform. In addition, it includes the real-time timing as well as the noise suppression results when the entire pipeline was run on the smartphone platform.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/EMBC.2014.6943733 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
On-Chip Metamaterial-Enhanced Mid-Infrared Photodetectors with Built-In Encryption Features.
Adv Sci (Weinh)
January 2025
College of Physics and Optoelectronic Engineering, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, No. 1, Sub-Lane Xiangshan, Xihu District, Hangzhou, 310024, China.
The integration of mid-infrared (MIR) photodetectors with built-in encryption capabilities holds immense promise for advancing secure communications in decentralized networks and compact sensing systems. However, achieving high sensitivity, self-powered operation, and reliable performance at room temperature within a miniaturized form factor remains a formidable challenge, largely due to constraints in MIR light absorption and the intricacies of embedding encryption at the device level. Here, a novel on-chip metamaterial-enhanced, 2D tantalum nickel selenide (Ta₂NiSe₅)-based photodetector, meticulously designed with a custom-engineered plasmonic resonance microstructure to achieve self-powered photodetection in the nanoampere range is unveiled.
View Article and Find Full Text PDFPerceptions of Machine Learning among Therapists Practicing Applied Behavior Analysis: A National Survey.
Behav Anal Pract
December 2024
Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI USA.
Unlabelled: Collecting data and logging behaviors of clients who have autism spectrum disorder (ASD) during applied behavior analysis (ABA) therapy sessions can be challenging in real time, especially when the behaviors require a rapid response, like self-injury or aggression. Little information is available about the automation of data collection in ABA therapy, such as through machine learning (ML). Our survey of ABA therapists nationally revealed mixed levels of familiarity with ML and generally neutral responses to statements endorsing the benefits of ML.
View Article and Find Full Text PDFA data management system for precision medicine.
PLOS Digit Health
January 2025
Clinical Care & Research, ORTEC B.V., Zoetermeer, The Netherlands.
Precision, or personalised medicine has advanced requirements for medical data management systems (MedDMSs). MedDMS for precision medicine should be able to process hundreds of parameters from multiple sites, be adaptable while remaining in sync at multiple locations, real-time syncing to analytics and be compliant with international privacy legislation. This paper describes the LogiqSuite software solution, aimed to support a precision medicine solution at the patient care (LogiqCare), research (LogiqScience) and data science (LogiqAnalytics) level.
View Article and Find Full Text PDFBrain-inspired learning rules for spiking neural network-based control: a tutorial.
Biomed Eng Lett
January 2025
Department of Computer Engineering, Kwangwoon University, Seoul, 01897 Republic of Korea.
Robotic systems rely on spatio-temporal information to solve control tasks. With advancements in deep neural networks, reinforcement learning has significantly enhanced the performance of control tasks by leveraging deep learning techniques. However, as deep neural networks grow in complexity, they consume more energy and introduce greater latency.
View Article and Find Full Text PDFAdvancements in Passive Wireless Sensing Systems in Monitoring Harsh Environment and Healthcare Applications.
Nanomicro Lett
January 2025
RFIC Bio Centre, Kwangwoon University, Seoul, 01897, South Korea.
Recent advancements in passive wireless sensor technology have significantly extended the application scope of sensing, particularly in challenging environments for monitoring industry and healthcare applications. These systems are equipped with battery-free operation, wireless connectivity, and are designed to be both miniaturized and lightweight. Such features enable the safe, real-time monitoring of industrial environments and support high-precision physiological measurements in confined internal body spaces and on wearable epidermal devices.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!