This paper presents an improved noise classification in environment-adaptive speech processing pipelines of cochlear implants. This improvement is achieved by using a dual-microphone and by using a computationally efficient feature-level combination approach to achieve real-time operation. A new measure named Suppression Advantage is also defined in order to quantify the noise suppression improvement of an entire pipeline due to noise classification. The noise classification and suppression improvement results are presented for four commonly encountered noise environments.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/EMBC.2013.6610742 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
EEG-to-EEG: Scalp-to-Intracranial EEG Translation Using a Combination of Variational Autoencoder and Generative Adversarial Networks.
Sensors (Basel)
January 2025
Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.
A generative adversarial network (GAN) makes it possible to map a data sample from one domain to another one. It has extensively been employed in image-to-image and text-to image translation. We propose an EEG-to-EEG translation model to map the scalp-mounted EEG (scEEG) sensor signals to intracranial EEG (iEEG) sensor signals recorded by foramen ovale sensors inserted into the brain.
View Article and Find Full Text PDFHeight Measurement Method for Meter-Wave Multiple Input Multiple Output Radar Based on Transmitted Signals and Receive Filter Design.
Sensors (Basel)
January 2025
Air and Missile Defense College, Air Force Engineering University, Xi'an 710051, China.
To address the issue of low-elevation target height measurement in the Multiple Input Multiple Output (MIMO) radar, this paper proposes a height measurement method for meter-wave MIMO radar based on transmitted signals and receive filter design, integrating beamforming technology and cognitive processing methods. According to the characteristics of beamforming technology forming nulls at interference locations, we assume that the direct wave and reflected wave act as interference signals and hypothesize a direction for a hypothetical target. Then, the data received are processed to obtain the height of low-elevation-angle targets using a cognitive approach that jointly optimizes the transmitted signal and receive filter.
View Article and Find Full Text PDFResearch on the Classification of Traditional Building Materials in Southern Fujian Using the Reflection Intensity Values of Ground-Based LiDAR.
Sensors (Basel)
January 2025
Department of Civil Engineering and Engineering Management, National Quemoy University, Kinmen 89250, Taiwan.
Ground-based LiDAR technology has been widely applied in various fields for acquiring 3D point cloud data, including spatial coordinates, digital color information, and laser reflectance intensities (I-values). These datasets preserve the digital information of scanned objects, supporting value-added applications. However, raw point cloud data visually represent spatial features but lack attribute information, posing challenges for automated object classification and effective management.
View Article and Find Full Text PDFA Line of Sight/Non Line of Sight Recognition Method Based on the Dynamic Multi-Level Optimization of Comprehensive Features.
Sensors (Basel)
January 2025
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
With the advent of the 5G era, high-precision localization based on mobile communication networks has become a research hotspot, playing an important role in indoor emergency rescue in shopping malls, smart factory management and tracking, as well as precision marketing. However, in complex environments, non-line-of-sight (NLOS) propagation reduces the measurement accuracy of 5G signals, causing large deviations in position solving. In order to obtain high-precision position information, it is necessary to recognize the propagation state of the signal before distance measurement or angle measurement.
View Article and Find Full Text PDFA knowledge embedded multimodal pseudo-siamese model for atrial fibrillation detection.
Sci Rep
January 2025
School of Computer Science and Engineering, Changchun University of Technology, Changchun, 130102, People's Republic of China.
Atrial fibrillation (AF) is a common arrhythmia disease with a higher incidence rate. The diagnosis of AF is time-consuming. Although many ECG classification models have been proposed to assist in AF detection, they are prone to misclassifying indistinguishable noise signals, and the context information of long-term signals is also ignored, which impacts the performance of AF detection.
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!