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 SOLVING THE LONG-TAILED PROBLEM VIA INTRA- AND INTER-CATEGORY BALANCE
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- Read more about SPEECHSPLIT2.0: UNSUPERVISED SPEECH DISENTANGLEMENT FOR VOICE CONVERSION WITHOUT TUNING AUTOENCODER BOTTLENECKS
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- Read more about CDX-Net: Cross-Domain Multi-Feature Fusion Modeling via Deep Neural Networks for Multivariate Time Series Forecasting in AIOps
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- Read more about Integrating multiple ASR systems into NLP backend with attention fusion
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- Read more about On Adversarial Robustness of Large-scale Audio Visual Learning
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- Read more about MULTIVARIATE MULTISCALE COSINE SIMILARITY ENTROPY
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The rapid development in sensor technology has made it convenient to acquire data from multi-channel systems but has also highlighted the need for the analysis of nonlinear dynamical properties at a higher level - the so-called structural complexity. Traditional single-scale entropy measures, such as the amplitude based Sample Entropy (SampEn), are designed to give a quantification of irregularity and randomness.
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- Read more about Slides of "LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning"
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This paper deals with the problem of localization in a cellular network in a dense urban scenario. Global Navigation Satellite Systems (GNSS) typically perform poorly in urban environments, where the likelihood of line-of-sight conditions is low, and thus alternative localization methods are required for good accuracy.
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- Read more about BNU: A BALANCE-NORMALIZATION-UNCERTAINTY MODEL FOR INCREMENTAL EVENT DETECTION
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Event detection is challenging in real-world application since new events continually occur and old events still exist which may result in repeated labeling for old events. There- fore, incremental event detection is essential where a model continuously learns new events and meanwhile prevents per- formance from degrading on old events.
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- Read more about Poster of "LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning"
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This paper deals with the problem of localization in a cellular network in a dense urban scenario. Global Navigation Satellite Systems (GNSS) typically perform poorly in urban environments, where the likelihood of line-of-sight conditions is low, and thus alternative localization methods are required for good accuracy.
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