The acoustic theory for multichannel sound reproduction systems usually assumes free-field conditions for the listening environment. However, their performance in real-world listening environments may be impaired by reflections at the walls. This impairment can be reduced by suitable compensation measures. For systems with many channels, active compensation is an option, since the compensating waves can be created by the reproduction loudspeakers. Due to the time-varying nature of room acoustics, the compensation signals have to be determined by an adaptive system. The problems associated with the successful operation of multichannel adaptive systems are addressed in this contribution. First, a method for decoupling the adaptation problem is introduced. It is based on a generalized singular value decomposition and is called eigenspace adaptive filtering. Unfortunately, it cannot be implemented in its pure form, since the continuous adaptation of the generalized singular value decomposition matrices to the variable room acoustics is numerically very demanding. However, a combination of this mathematical technique with the physical description of wave propagation yields a realizable multichannel adaptation method with good decoupling properties. It is called wave domain adaptive filtering and is discussed here in the context of wave field synthesis.
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Comput Biol Med
January 2025
University of Rwanda, Rwanda. Electronic address:
Deep learning methods have significantly improved medical image analysis, particularly in detecting COVID-19 chest X-rays. Nonetheless, these methodologies frequently inhibit some drawbacks, such as limited interpretability, extensive computational resources, and the need for extensive datasets. To tackle these issues, we introduced two novel algorithms: the Dynamic Co-Occurrence Grey Level Matrix (DC-GLM) and the Contextual Adaptation Multiscale Gabor Network (CAMSGNeT).
View Article and Find Full Text PDFPLoS One
January 2025
Computer Engineering, CCSIT, King Faisal University, Al Hufuf, Kingdom of Saudi Arabia.
This paper presents a low-power, second-order composite source-follower-based filter architecture optimized for biomedical signal processing, particularly ECG and EEG applications. Source-follower-based filters are recommended in the literature for high-frequency applications due to their lower power consumption when compared to filters with alternative topologies. However, they are not suitable for biomedical applications requiring low cutoff frequencies as they are designed to operate in the saturation region.
View Article and Find Full Text PDFJ Exp Psychol Hum Percept Perform
January 2025
School of Psychology, University of Sussex.
Human listeners have a remarkable capacity to adapt to severe distortions of the speech signal. Previous work indicates that perceptual learning of degraded speech reflects changes to sublexical representations, though the precise format of these representations has not yet been established. Inspired by the neurophysiology of auditory cortex, we hypothesized that perceptual learning involves changes to perceptual representations that are tuned to acoustic modulations of the speech signal.
View Article and Find Full Text PDFBiotechnol Prog
January 2025
Biologics Technology Research Laboratories, Biologics Division, Daiichi Sankyo Co., Ltd, Oura-gun, Gunma, Japan.
Virus removal by filtration is a crucial step in ensuring the safety of therapeutic antibodies and other biopharmaceutical products by mitigating the risk of endogenous and adventitious viral contamination. However, there are monoclonal antibodies (mAb) that are difficult to filter effectively using virus removal filters (i.e.
View Article and Find Full Text PDFNeural Netw
January 2025
Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China; Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China. Electronic address:
Anomaly detection on graph data has garnered significant interest from both the academia and industry. In recent years, fueled by the rapid development of Graph Neural Networks (GNNs), various GNNs-based anomaly detection methods have been proposed and achieved good results. However, GNNs-based methods assume that connected nodes have similar classes and features, leading to issues of class inconsistency and semantic inconsistency in graph anomaly detection.
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