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Found 20 papers, 14 papers with code
Cascaded Cross-Modal Transformer for Audio-Textual Classification
Subsequently, we combine language-specific Bidirectional Encoder Representations from Transformers (BERT) with Wav2Vec2. 0 audio features via a novel cascaded cross-modal transformer (CCMT).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
ICASSP 2023 Acoustic Echo Cancellation Challenge
This is the fourth AEC challenge and it is enhanced by adding a second track for personalized acoustic echo cancellation, reducing the algorithmic + buffering latency to 20ms, as well as including a full-band version of AECMOS.
RoDia: A New Dataset for Romanian Dialect Identification from Speech
We introduce RoDia, the first dataset for Romanian dialect identification from speech.
CL-MAE: Curriculum-Learned Masked Autoencoders
In this paper, we propose a curriculum learning approach that updates the masking strategy to continually increase the complexity of the self-supervised reconstruction task.
Cascaded Cross-Modal Transformer for Request and Complaint Detection
We propose a novel cascaded cross-modal transformer (CCMT) that combines speech and text transcripts to detect customer requests and complaints in phone conversations.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors
We propose an efficient abnormal event detection model based on a lightweight masked auto-encoder (AE) applied at the video frame level.
Ranked #12 on
Anomaly Detection
on UCSD Ped2
Sea Ice Segmentation From SAR Data by Convolutional Transformer Networks
Sea ice is a crucial component of the Earth's climate system and is highly sensitive to changes in temperature and atmospheric conditions.
DeepVQE: Real Time Deep Voice Quality Enhancement for Joint Acoustic Echo Cancellation, Noise Suppression and Dereverberation
Acoustic echo cancellation (AEC), noise suppression (NS) and dereverberation (DR) are an integral part of modern full-duplex communication systems.
Lightning Fast Video Anomaly Detection via Adversarial Knowledge Distillation
We propose a very fast frame-level model for anomaly detection in video, which learns to detect anomalies by distilling knowledge from multiple highly accurate object-level teacher models.
Ranked #20 on
Anomaly Detection
on CUHK Avenue
Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection
In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss.
Ranked #5 on
Anomaly Detection
on CUHK Avenue
Learning Rate Curriculum
In this work, we propose a novel curriculum learning approach termed Learning Rate Curriculum (LeRaC), which leverages the use of a different learning rate for each layer of a neural network to create a data-agnostic curriculum during the initial training epochs.
Ranked #4 on
Speech Emotion Recognition
on CREMA-D
Guided deep learning by subaperture decomposition: ocean patterns from SAR imagery
Overall, we encourage the development of data centring approaches, showing that, data preprocessing could bring significant performance improvements over existing deep learning models.
Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-Resolution
Our attention module uses the convolution operation to perform joint spatial-channel attention on multiple concatenated input tensors, where the kernel (receptive field) size controls the reduction rate of the spatial attention, and the number of convolutional filters controls the reduction rate of the channel attention, respectively.
Ranked #1 on
Image Super-Resolution
on IXI
SepTr: Separable Transformer for Audio Spectrogram Processing
Following the successful application of vision transformers in multiple computer vision tasks, these models have drawn the attention of the signal processing community.
Ranked #1 on
Time Series Analysis
on Speech Commands
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection
Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field.
Ranked #1 on
Anomaly Detection
on CUHK Avenue
(TBDC metric)
CyTran: A Cycle-Consistent Transformer with Multi-Level Consistency for Non-Contrast to Contrast CT Translation
Our neural model can be trained on unpaired images, due to the integration of a multi-level cycle-consistency loss.
Self-paced ensemble learning for speech and audio classification
Instead of just combining the models, we propose a self-paced ensemble learning scheme in which models learn from each other over several iterations.
Ranked #6 on
Speech Emotion Recognition
on CREMA-D
Emotion Recognition System from Speech and Visual Information based on Convolutional Neural Networks
In order to increase the accuracy of the recognition system, we analyze also the speech data and fuse the information coming from both sources, i. e., visual and audio.
Non-linear Neurons with Human-like Apical Dendrite Activations
In order to classify linearly non-separable data, neurons are typically organized into multi-layer neural networks that are equipped with at least one hidden layer.
Ranked #8 on
Speech Emotion Recognition
on CREMA-D
