
- Read more about Identification of Edge Disconnections in Networks Based on Graph Filter Outputs
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Graphs are fundamental mathematical structures used in various fields to model statistical and physical relationships between data, signals, and processes. In some applications, such as data processing in graphs that represent physical networks, the initial network topology is known. However, disconnections of edges in the network change the topology and may affect the signals and processes over the network. In this paper, we consider the problem of edge disconnection identification in networks by using concepts from graph signal processing.
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- Read more about DIFFICULTY-AWARE NEURAL BAND-TO-PIANO SCORE ARRANGEMENT BASED ON NOTE- AND STATISTIC-LEVEL CRITERIA
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- Read more about Light-SERNet: A Lightweight Fully Convolutional Neural Network for Speech Emotion Recognition
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Detecting emotions directly from a speech signal plays an important role in effective human-computer interactions. Existing speech emotion recognition models require massive computational and storage resources, making them hard to implement concurrently with other machine-interactive tasks in embedded systems. In this paper, we propose an efficient and lightweight fully convolutional neural network (FCNN) for speech emotion recognition in systems with limited hardware resources.
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- Read more about CNN-AIDED FACTOR GRAPHS WITH ESTIMATED MUTUAL INFORMATION FEATURES FOR SEIZURE DETECTION
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- Read more about CNN-AIDED FACTOR GRAPHS WITH ESTIMATED MUTUAL INFORMATION FEATURES FOR SEIZURE DETECTION
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We propose a convolutional neural network (CNN) aided factor graphs assisted by mutual information features estimated by a neural network for seizure detection. Specifically, we use neural mutual information estimation to evaluate the correlation between different electroencephalogram (EEG) channels as features. We then use a 1D-CNN to extract extra features from the EEG signals and use both features to estimate the probability of a seizure event. Finally, learned factor graphs are employed to capture the temporal correlation in the signal.
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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 LEARNING TO PREDICT SPEECH IN SILENT VIDEOS VIA AUDIOVISUAL ANALOGY
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- Read more about Quantum Federated Learning with Quantum Data
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Mahdi Poster.pdf

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- Read more about Phonotactic Language Recognition using a Universal Phoneme Recognizer and a Transformer Architecture
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In this paper, we describe a phonotactic language recognition model that effectively manages long and short n-gram input sequences to learn contextual phonotacticbased vector embeddings. Our approach uses a transformerbased encoder that integrates a sliding window attention to attempt finding discriminative short and long cooccurrences of language dependent n-gram phonetic units. We then evaluate and compare the use of different phoneme recognizers (Brno and Allosaurus) and sub-unit tokenizers to help select the more discriminative n-grams.
Poster.pdf

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