Dependence of the performance of feedforward active duct noise control on secondary loudspeaker parameters is investigated. Noise reduction performance can be improved if the force factor of the secondary loudspeaker is higher. For example, broadband noise reduction improvement up to 1.6 dB is predicted by increasing the force factor by 50%. In addition, a secondary loudspeaker with a larger force factor was found to have quicker convergence in the adaptive algorithm in experiment. In simulations, noise reduction is improved in using an adaptive algorithm by using a secondary loudspeaker with a heavier moving mass. It is predicted that an extra broadband noise reduction of more than 7 dB can be gained using an adaptive filter if the force factor, moving mass and coil inductance of a commercially available loudspeaker are doubled. Methods to increase the force factor beyond those of commercially available loudspeakers are proposed.
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J Acoust Soc Am
December 2024
Key Laboratory of Modern Acoustics and Institute of Acoustics, Nanjing University, 22 Hankou Road, Nanjing 210093, China.
Global active noise control (ANC) systems reduce noise over the entire car cabin with robust performance even as the human head moves; however, they have not been implemented in real-world applications. A robust error sensing strategy is proposed in this paper that is based on which a feasible global ANC system is realized in an electric car, and real-time ANC experiments demonstrate its effectiveness. Simulations based on measured road noise show that using evenly distributed error sensors is a robust error sensing strategy for different car speeds and the upper limit frequency of 3 dB global control is inversely proportional to the equivalent distance between error sensors.
View Article and Find Full Text PDFSemin Hear
May 2023
Department of Hearing and Speech Sciences, Vanderbilt University, Nashville, Tennessee.
Response time-based dual-task paradigms are commonly adopted to measure behavioral listening effort. Most extant studies used an all-response approach that included secondary task responses under both correct and incorrect primary task responses during analysis. However, evidence supporting this strategy is limited.
View Article and Find Full Text PDFNeural Netw
January 2023
Department of Computer Science and Engineering, Ohio State University, Columbus, OH 43210-1277, USA; Center for Cognitive and Brain Sciences, Ohio State University, Columbus, OH 43210-1277, USA. Electronic address:
Traditional multi-channel active noise control (MCANC) is based on adaptive filtering and usually uses a separate control unit for each channel. This paper introduces a deep learning based approach for multi-channel active noise control (ANC). The proposed approach, called deep MCANC, encodes optimal control parameters corresponding to different noises and environments, and jointly computes the multiple canceling signals to cancel or attenuate the primary noises captured at error microphones.
View Article and Find Full Text PDFClin Psychol Eur
June 2022
Department of Psychology and Psychotherapy, Universität Witten/Herdecke, Witten, Germany.
Background: Recent findings indicated that mental disorders are associated with both an up-regulation of negative affect and a down-regulation of positive affect (PA) as distinct processes. Established treatment approaches focus on the modification of problems and negative affect only. Experimental paradigms in healthy samples and research on strengths-based approaches showed that fostering PA may improve psychotherapy process and outcome.
View Article and Find Full Text PDFFront Hum Neurosci
April 2022
Applied Neuroscience Laboratory, Universidade Federal de Pernambuco, Recife, Brazil.
Background: We investigated whether transcranial magnetic stimulation (rTMS) over the primary somatosensory cortex (S1) and sensory stimulation (SS) could promote upper limb recovery in participants with subacute stroke.
Methods: Participants were randomized into four groups: rTMS/Sham SS, Sham rTMS/SS, rTMS/SS, and control group (Sham rTMS/Sham SS). Participants underwent ten sessions of sham or active rTMS over S1 (10 Hz, 1,500 pulses, 120% of resting motor threshold, 20 min), followed by sham or active SS.
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