Papers channel selection
“channel selection” 태그가 달린 논문 119편 · 필터 해제
NeuroXAI: Adaptive, robust, explainable surrogate framework for determination of channel importance in EEG application
Electroencephalogram (EEG)-based applications often require numerous channels to achieve high performance, which limits their widespread use. Various channel selection methods have been proposed to identify minimum EEG c…
channel selectionEEGElectroencephalogram (EEG)Explainable Artificial Intelligence (XAI)+1Lightweight and Robust Multi-Channel End-to-End Speech Recognition with Spherical Harmonic Transform
This paper presents SHTNet, a lightweight spherical harmonic transform (SHT) based framework, which is designed to address cross-array generalization challenges in multi-channel automatic speech recognition (ASR) through…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)channel selectionspeech-recognition+1SepPrune: Structured Pruning for Efficient Deep Speech Separation
Although deep learning has substantially advanced speech separation in recent years, most existing studies continue to prioritize separation quality while overlooking computational efficiency, an essential factor for low…
channel selectionComputational EfficiencySpeech SeparationDifferentiable Channel Selection in Self-Attention For Person Re-Identification
In this paper, we propose a novel attention module termed the Differentiable Channel Selection Attention module, or the DCS-Attention module. In contrast with conventional self-attention, the DCS-Attention module feature…
channel selectionNeural Architecture SearchPerson Re-IdentificationA Real-time and Hardware Efficient Artfecat-free Spike Sorting Using Deep Spike Detection
Spike sorting is a valuable tool in understanding brain regions. It assigns detected spike waveforms to their origins, helping to research the mechanism of the human brain and the development of implantable brain-machine…
channel selectionSpike SortingParameter-Free Fine-tuning via Redundancy Elimination for Vision Foundation Models
Vision foundation models (VFMs) are large pre-trained models that form the backbone of various vision tasks. Fine-tuning VFMs can further unlock their potential for downstream tasks or scenarios. However, VFMs often cont…
channel selectionGPUDynamic Gradient Sparse Update for Edge Training
Training on edge devices enables personalized model fine-tuning to enhance real-world performance and maintain data privacy. However, the gradient computation for backpropagation in the training requires significant memo…
channel selectionOuroMamba: A Data-Free Quantization Framework for Vision Mamba Models
We present OuroMamba, the first data-free post-training quantization (DFQ) method for vision Mamba-based models (VMMs). We identify two key challenges in enabling DFQ for VMMs, (1) VMM's recurrent state transitions restr…
channel selectionContrastive LearningData Free QuantizationGPU+3Training Channel Selection for Learning-based 1-bit Precoding in Massive MU-MIMO
Learning-based algorithms have gained great popularity in communications since they often outperform even carefully engineered solutions by learning from training samples. In this paper, we show that the selection of app…
channel selectionSlimSeiz: Efficient Channel-Adaptive Seizure Prediction Using a Mamba-Enhanced Network
Epileptic seizures cause abnormal brain activity, and their unpredictability can lead to accidents, underscoring the need for long-term seizure prediction. Although seizures can be predicted by analyzing electroencephalo…
channel selectionEEGElectroencephalogram (EEG)Mamba+3PAMLR: A Passive-Active Multi-Armed Bandit-Based Solution for LoRa Channel Allocation
Achieving low duty cycle operation in low-power wireless networks in urban environments is complicated by the complex and variable dynamics of external interference and fading. We explore the use of reinforcement learnin…
channel selectionJoint Channel Selection using FedDRL in V2X
Vehicle-to-everything (V2X) communication technology is revolutionizing transportation by enabling interactions between vehicles, devices, and infrastructures. This connectivity enhances road safety, transportation effic…
channel selectionDecision MakingDeep Reinforcement LearningGeometry-Constrained EEG Channel Selection for Brain-Assisted Speech Enhancement
Brain-assisted speech enhancement (BASE) aims to extract the target speaker in complex multi-talker scenarios using electroencephalogram (EEG) signals as an assistive modality, as the auditory attention of the listener c…
channel selectionEEGElectroencephalogram (EEG)Speech EnhancementOptimizing food taste sensory evaluation through neural network-based taste electroencephalogram channel selection
The taste electroencephalogram (EEG) evoked by the taste stimulation can reflect different brain patterns and be used in applications such as sensory evaluation of food. However, considering the computational cost and ef…
channel selectionEEGElectroencephalogram (EEG)Learning Binary Color Filter Arrays with Trainable Hard Thresholding
Color Filter Arrays (CFA) are optical filters in digital cameras that capture specific color channels. Current commercial CFAs are hand-crafted patterns with different physical and application-specific considerations. Th…
channel selectionDemosaickingImproving the Evaluation and Actionability of Explanation Methods for Multivariate Time Series Classification
Explanation for Multivariate Time Series Classification (MTSC) is an important topic that is under explored. There are very few quantitative evaluation methodologies and even fewer examples of actionable explanation, whe…
channel selectionTime SeriesTime Series ClassificationReference Channel Selection by Multi-Channel Masking for End-to-End Multi-Channel Speech Enhancement
In end-to-end multi-channel speech enhancement, the traditional approach of designating one microphone signal as the reference for processing may not always yield optimal results. The limitation is particularly in scenar…
channel selectionSpeech EnhancementNaturalistic Music Decoding from EEG Data via Latent Diffusion Models
In this article, we explore the potential of using latent diffusion models, a family of powerful generative models, for the task of reconstructing naturalistic music from electroencephalogram (EEG) recordings. Unlike sim…
channel selectionEEGElectroencephalogram (EEG)DeltaKWS: A 65nm 36nJ/Decision Bio-inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC with 0.6V Near-Threshold SRAM
This paper introduces DeltaKWS, to the best of our knowledge, the first $\Delta$RNN-enabled fine-grained temporal sparsity-aware KWS IC for voice-controlled devices. The 65 nm prototype chip features a number of techniqu…
channel selectionKeyword SpottingQuantizationQuantifying Spatial Domain Explanations in BCI using Earth Mover's Distance
Brain-computer interface (BCI) systems facilitate unique communication between humans and computers, benefiting severely disabled individuals. Despite decades of research, BCIs are not fully integrated into clinical and …
Brain Computer Interfacechannel selectionEEGMotor Imagery