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IEEE ICASSP 2024

IEEE ICASSP 2024 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The IEEE ICASSP 2024 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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[Poster] A Variable Smoothing for Nonconvexly Constrained Nonsmooth Optimization with Application to Sparse Spectral Clustering
FOLLOWING THE EMBEDDING: IDENTIFYING TRANSITION PHENOMENA IN WAV2VEC 2.0 REPRESENTATIONS OF SPEECH AUDIO
LEARNING SPECTRAL CANONICAL F-CORRELATION REPRESENTATION FOR FACE SUPER-RESOLUTION
Unsupervised Optimal Power Flow using Graph Neural Networks
Investigating End-to-end ASR Architectures for Long form Audio Transcription
DEMUCS for data-driven RF signal denoising
Dynamic Speech Emotion Recognition using a Conditional Neural Process
FEATURE-CONSTRAINED AND ATTENTION-CONDITIONED DISTILLATION LEARNING FOR VISUAL ANOMALY DETECTION
[Poster] Contrastive Deep Nonnegative Matrix Factorization For Community Detection
[Poster] Contrastive Deep Nonnegative Matrix Factorization For Community Detection
ENHANCING NOISY LABEL LEARNING VIA UNSUPERVISED CONTRASTIVE LOSS WITH LABEL CORRECTION BASED ON PRIOR KNOWLEDGE
Detecting Check-Worthy Claims in Political Debates, Speeches, and Interviews Using Audio Data - Poster
Detecting Check-Worthy Claims in Political Debates, Speeches, and Interviews Using Audio Data - Presentation
TOWARDS MULTI-DOMAIN FACE LANDMARK DETECTION WITH SYNTHETIC DATA FROM DIFFUSION MODEL
[poster] Improving Design of Input Condition Invariant Speech Enhancement
[slides] Generation-Based Target Speech Extraction with Speech Discretization and Vocoder
UNIT-DSR: DYSARTHRIC SPEECH RECONSTRUCTION SYSTEM USING SPEECH UNIT NORMALIZATION
Poster
ENHANCING MULTILINGUAL TTS WITH VOICE CONVERSION BASED DATA AUGMENTATION AND POSTERIOR EMBEDDING
A SOUND APPROACH: Using Large Language Models to generate audio descriptions for egocentric text-audio retrieval - Poster

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