ICASSP 2022 Documents | SigPort

ICASSP 2022

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.

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Fast Graph Sampling for Short Video Summarization Using Gershgorin Disc Alignment

Deep Actor-Critic for Continuous 3D Motion Control in Mobile Relay Beamforming Networks

HAVE BEST OF BOTH WORLDS: TWO-PASS HYBRID AND E2E CASCADING FRAMEWORK FOR SPEECH RECOGNITION

FUSION OF MODULATION SPECTRAL AND SPECTRAL FEATURES WITH SYMPTOM METADATA FOR IMPROVED SPEECH-BASED COVID-19 DETECTION

PremiUm-CNN: Propagating Uncertainty Towards Robust Convolutional Neural Networks

UNIVERSAL PARALINGUISTIC SPEECH REPRESENTATIONS USING SELF-SUPERVISED CONFORMERS - ICASSP 2022 Poster

Universal Paralinguistic Speech Representations using Self-Supervised Conformers - ICASSP 2022 slides

DATA INCUBATION — SYNTHESIZING MISSING DATA FOR HANDWRITING RECOGNITION

Privacy attacks for automatic speech recognition acoustic models in a federated learning framework

LEARNING ADJUSTABLE IMAGE RESCALING WITH JOINT OPTIMIZATION OF PERCEPTION AND DISTORTION

[ICASSP 2022] NOT ALL FEATURES ARE EQUAL: SELECTION OF ROBUST FEATURES FOR SPEECH EMOTION RECOGNITION IN NOISY ENVIRONMENTS

Generalized autocorrelation analysis for multi-target detection

Fast and Stable Convergence of Online SGD for CV@R-based Risk-Aware Statistical Learning

Generalized autocorrelation analysis for multi-target detection

WAV2VEC-SWITCH: CONTRASTIVE LEARNING FROM ORIGINAL-NOISY SPEECH PAIRS FOR ROBUST SPEECH RECOGNITION

Leveraging Local Temporal Information For Multimodal Scene Classification

DeepFilterNet: A Low Complexity Speech Enhancement Framework for Full-Band Audio based on Deep Filtering

SPARSE SUBSPACE TRACKING IN HIGH DIMENSIONS

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