ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The ICASSP 2022 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit the website.
- Read more about DYNAMIC TEXTURE RECOGNITION USING PDV HASHING AND DICTIONARY LEARNING ON MULTI-SCALE VOLUME LOCAL BINARY PATTERN
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Spatial-temporal local binary pattern (STLBP) has been widely used in dynamic texture recognition. STLBP often encounters the high-dimension problem as its dimension increases exponentially, so that STLBP could only utilize a small neighborhood. To tackle this problem, we propose a method for dynamic texture recognition using PDV hashing and dictionary learning on multi-scale volume local binary pattern (PHD-MVLBP).
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- Read more about SPATIAL-CONTEXT-AWARE DEEP NEURAL NETWORK FOR MULTI-CLASS IMAGE CLASSIFICATION
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- Read more about Internet Streaming Audio Based Speech Perception Threshold Measurement in Cochlear Implant Users
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Traditional face-to-face subjective listening test has become a challenge due to the COVID-19 pandemic. We developed a remote assessment system with Tencent Meeting, a video conferencing application, to address this issue. This paper
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- Read more about Closed-form single source direction-of-arrival estimator using first-order relative harmonic coefficients
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- Read more about Graph Convolutional Network Based Semi-Supervised Learning on Multi-Speaker Meeting Data
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- Read more about END-TO-END NETWORK BASED ON TRANSFORMER FOR AUTOMATIC DETECTION OF COVID-19
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- Read more about VARIATIONAL BAYESIAN FRAMEWORK FOR ADVANCED IMAGE GENERATION WITH DOMAIN-RELATED VARIABLES
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- Read more about VARIATIONAL BAYESIAN FRAMEWORK FOR ADVANCED IMAGE GENERATION WITH DOMAIN-RELATED VARIABLES
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- Read more about Human emotion recognition using multi-modal biological signals based on time lag-considered correlation maximization
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A human emotion recognition using multi-modal biological signals based on time lag-considered correlation maximization is presented in this paper. Various multi-modal emotion recognition methods for visual stimuli have been studied and they focus on gaze and brain activity data. The visual stimuli captured by human eyes are sent to the brain by neurotransmitters. Thus, there is a time lag between gaze data, which record where humans gaze at, and brain activity data. However, most of the previous methods only integrate features obtained from each data without considering such a time lag.
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