Single neurons can perform as well as dense networks in binary and multi-class recognition tasks.
problem Designing efficient neural networks for recognition tasks.
method Investigated the use of single or multiple neurons in neural networks for binary and multi-class recognition tasks.
result Sparse networks can be as efficient as dense networks in both binary and multi-class tasks.
MCLNN improves sound event recognition with fewer parameters.
problem Improving sound event recognition with deep neural networks.
method Developed MCLNN to enforce sparseness and frequency shift invariance.
result MCLNN achieved competitive performance with 12% fewer parameters.
This paper improves speech recognition models using deep neural networks.
problem Improving automatic speech recognition models.
method Deployment of residual, densely-connected, and highway networks into speech recognition.
result Significant improvements in speech recognition models.
Improved deep neural network training for emotion recognition across datasets.
problem Training deep neural networks with diverse datasets to avoid forgetting learned knowledge.
method Extended loss function incorporating information from similar networks trained on other datasets.
result Improved performance in emotion recognition across different datasets.
MCLNN improves sound recognition by learning frequency bands.
problem Sound recognition from neural networks often misses environmental sound specifics.
method MCLNN incorporates filterbank behavior and automates feature combination exploration.
result MCLNN outperforms state-of-the-art methods on ESC-10 dataset.
A new deep neural network improves short-speech recognition.
problem Improving speaker recognition for short-duration speech segments.
method Proposes UtterIdNet, a novel deep neural network architecture.
result Consistent and stable performance for short segments, especially sub-second durations.
Paper presents a neural network for open set recognition.
problem Open set recognition in security and other domains.
method Neural network representation for same class closeness and different class separation.
result Statistically significant improvement on three datasets.
Enhances hand gesture recognition with separate networks and shared features.
problem Improving recognition accuracy of unimodal 3D-CNNs for dynamic hand gestures.
method Separate networks for each modality, collaborative learning, spatiotemporal semantic alignment loss, focal regularization.
result Improves test time recognition accuracy and state-of-the-art performance.
Project extends emotion recognition database and trains neural networks for categorical and dimensional emotions.
problem Understanding and recognizing emotions for human-computer interaction, safety, and medical research.
method Training CNN + RNN models with emotion category and valence/arousal representations, comparing three model types.
result Categorical emotion recognition performance improves with combined model training.
New approach uses graphs for sign language recognition.
problem Challenges in recognizing sign language for deaf individuals.
method Spatial-Temporal Graph Convolutional Network.
result Improved sign language recognition using human skeletal movements.
Speech recognition simplified by treating audio as images.
problem Identifying voice commands in noisy environments.
method Comparing neural network architectures (CNN, low-latency CNN, adversarially trained CNN) for keyword spotting.
result Demonstrated how to apply image classification techniques to audio recognition.
Paper explores offline and online image recognition using neural networks.
problem Challenges in evolving image recognition through different settings.
method Used Convolutional Neural Networks and Multi-layer Perceptrons.
result Encouraging preliminary results in offline and online image classification.
Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) architecture that has been designed to address the vanishing and exploding gradient problems of conventional RNNs. Unlike feedforward neural networks, RNNs have cyclic connections making them powerful for modeling sequences. They have been successfully u…
Paper explores EEG-based speech recognition using transformers, showing faster training and better performance for smaller vocabularies.
problem Continuous speech recognition using EEG features.
method Transformer-based ASR model compared to RNN-based models.
result Transformer models perform better for smaller vocabularies but RNN models outperform them for larger vocabularies.
Improved deep learning for action recognition using multi-kernel SVM and deep neural networks.
problem Challenges in video understanding, especially action recognition, despite deep neural networks' success in image understanding.
method Combining multi-kernel SVM with a multi-stream deep convolutional neural network, including hand-crafted features.
result Achieved close to state-of-the-art performance on the HMDB-51 dataset.
Neural network framework for language recognition considers sequence information and improves accuracy.
problem Challenging task of automatic language identification in noisy conditions.
method Proposes a neural network framework with bidirectional LSTM and attention modeling for relevance weighting.
result Significant improvements over conventional methods in noisy conditions and multi-speaker speech.
Paper tackles zero-shot activity recognition using video features and text embeddings.
problem Zero-shot activity recognition with videos.
method Auto-encoder model for multimodal joint embedding, 3D convolutional action recognition for visual features, GloVe word embeddings for textual features.
result Improved zero-shot recognition results with top-n accuracy and mean Nearest Neighbor Overlap.
Learned feature representations and sub-phoneme posteriors from Deep Neural Networks (DNNs) have been used separately to produce significant performance gains for speaker and language recognition tasks. In this work we show how these gains are possible using a single DNN for both speaker and language recognition. The u…
Self-supervised ECG learning improves emotion recognition.
problem Improving emotion recognition from ECG signals.
method Multi-task deep learning framework with signal transformations as pretext tasks.
result Significant performance improvement in emotion classification.
Two attention models improve human activity recognition by focusing on important signals and sensor modalities.
problem Noise and unimportant signal components in recurrent networks for human activity recognition.
method Temporal and sensor attention mechanisms with continuity constraints.
result State-of-the-art results on three datasets, showing improved understandability and mean F1 score.
QLSTM improves speech recognition by considering internal quaternion dependencies.
problem Weak internal dependencies in traditional RNNs for speech recognition.
method Proposes QLSTM, a quaternion-based LSTM that considers both external and internal dependencies.
result QLSTM achieves better performance with up to 2.8 times fewer parameters.
Deep-learning method improves sEMG-based gesture recognition accuracy.
problem Inter-session and inter-subject variances in sEMG-based gesture recognition.
method Recurrent Neural Networks and Domain Adaptation method.
result Our approach outperforms state-of-the-art methods in recognition accuracy.
End-to-end speech recognition using CNNs and CTC.
problem Efficiently train end-to-end speech recognition systems.
method Combining hierarchical CNNs with CTC for sequence labeling.
result Proposed model is computationally efficient and competitive.
EdgeSpeechNets improve speech recognition on mobile devices.
problem Deploying deep learning for speech recognition on edge devices is challenging.
method Human-machine collaboration for designing efficient DNN architectures.
result EdgeSpeechNets achieve higher accuracy with smaller network size and lower computational cost.
Visualizes deep neural networks for speech recognition using learned topographic filter maps.
problem Unintuitive internal structure of deep neural networks complicates activation visualization.
method Trains a convolutional speech recognition model with filters arranged in a 2D grid, highlighting similar filters.
result Topographic filter maps visualize artificial neuron activations more intuitively.
Replicated attention model in image classification and fine-grained recognition.
problem Improving attention mechanisms in neural networks.
method Implemented the 'Learn to Pay Attention' model in convolutional neural networks.
result Successfully replicated results in image classification and fine-grained recognition.
MCLNN improves sound recognition by learning frequency bands.
problem Efficiently recognizing acoustic events from audio signals.
method MCLNN uses a binary mask to force sparseness in network weights, focusing on frequency bands.
result MCLNN achieves competitive performance in sound recognition compared to state-of-the-art methods.
A deep historical LSTM model improves tennis shot recognition from RGB videos.
problem Improving action recognition from RGB video input for sports analysis.
method Convolutional Neural Network (CNN) and Weighted Long Short-Term Memory (LSTM) for 3D tennis shot recognition.
result The method achieves better performance than state-of-the-art baselines for tennis shot recognition.
Quaternion neural networks improve distant speech recognition.
problem Challenges in distant speech recognition due to noise and reverberation.
method Quaternion neural networks process multi-channel audio signals as quaternion entities, capturing internal and external dependencies.
result QLSTM outperforms real-valued LSTM on multi-channel distant speech recognition tasks.
Boosts neural network performance by improving weight separability.
problem Improving the separability of weight vectors in neural networks.
method Proposes a new evaluation metric and feed-backward reconstruction loss to encourage weight separability.
result Improves visual recognition performance across various tasks.
A framework uses deep learning for activity recognition in IoT devices.
problem Activity recognition in IoT devices without physical contact.
method Background subtraction followed by 3D-Convolutional Neural Networks.
result Enhanced activity recognition using small IoT devices.
Paper proposes a CNN for speech emotion recognition using center loss and reconstruction.
problem Speech emotion recognition (SER) in audio signals.
method Convolutional Neural Network (CNN) with center loss and reconstruction as regularizers.
result Proposed method achieves highly discriminative features for SER.
CSI-Net learns WiFi signals for body characterization and pose recognition.
problem Unified learning of body characteristics and pose recognition.
method Unified Deep Neural Network (DNN) for WiFi signal representation and multi-task learning.
result CSI-Net solves biometrics estimation and person recognition.
Improved deep neural networks for text-independent speaker recognition.
problem Text-independent speaker recognition using deep neural networks.
method Angular softmax activation, residual frame level connections, cosine similarity, discriminative similarity metric learning.
result Improved speaker recognition accuracy on real-life conditions.
A new HAR algorithm uses U-Net for pixel-level gesture recognition.
problem Multi-class window problem in traditional HAR methods.
method U-Net network for activity labeling and prediction at each sampling point.
result Highest accuracy and F1-score compared to other methods.
Deep belief network improves smartphone activity recognition.
problem Activity recognition on mobile devices.
method Categorization through deep belief network.
result 98.25% correct diagnosis in training data, 93.01% in test data.
This paper improves human activity recognition using LSTM-RNN models.
problem Improving accuracy in human activity recognition from sensor data.
method Design and training of LSTM-RNN models on WISDM dataset.
result Achieved an accuracy of above 94% and a loss of less than 30% in 500 epochs.
A new multi-layer attention mechanism improves speech keyword recognition accuracy.
problem Inaccurate attention weights in LSTM networks for speech keyword recognition.
method Introducing information from layers prior to feature extraction into attention weights calculations.
result The proposed multi-layer attention mechanism leads to more accurate attention weights and improved keyword spotting performance.
Paper explores deep learning features for complex emotion recognition.
problem Improving emotion recognition accuracy in complex emotions.
method Used pretrained networks (AudioSet Net, VoxCeleb Net, Deep Speech Net) and their deep layer features for emotion recognition.
result Achieved highest F1 score of 0.85 on EmoReact dataset.
Large receptive field CNNs improve distant speech recognition.
problem Degrading performance of ASR systems in noisy environments.
method Investigated large receptive field CNN variants including recursive, dilated, and hourglass networks.
result Stacked hourglass networks show significant improvements in distant speech recognition.
Speech emotion recognition system using features and text.
problem Improving accuracy in emotion recognition from speech.
method Used speech features (Spectrogram, MFCC) and text, trained Deep Neural Networks.
result Combined MFCC-Text CNN model achieved highest accuracy.
Transform classical network structures to graph CNN for better graph recognition.
problem Transforming classical network structures to graph CNN for better graph recognition.
method Review and introduce ResNet, Inception, and DenseNet into graph CNN, constructing G_ResNet, G_Inception, G_DenseNet.
result Demonstrated how different network structures work on graph CNN in the graph recognition task.
Improves neural network mapping functionality using latent feature generation.
problem Improving neural network performance in visual recognition tasks.
method Reversible learning for generating and learning latent features.
result The proposed method outperforms existing state-of-the-art methods in visual recognition.
New system uses microwave radar for hand gesture recognition, improving accuracy and reducing processing time.
problem Limited vision-based hand gesture recognition under dark conditions.
method Deformable deep convolutional generative adversarial network (DCGAN) on Doppler radar signals.
result Recognition rate improved by 10% and testing time reduced by 30%.
New dataset for UAV gesture recognition in outdoor settings.
problem Lack of public outdoor UAV gesture recognition datasets.
method Recorded 13 gestures in an outdoor setting, created 119 HD video clips, annotated with body joints and gesture classes.
result Baseline gesture recognition performance of 91.9% using P-CNN.
System supports 102 languages with deep neural networks, reducing error rates and improving recognition speed.
problem Online handwriting recognition for multiple languages with high accuracy and speed.
method Deep neural network architecture, Bézier curves for input encoding, sequence recognition methods.
result Reduced error rates by 20%-40% for most languages, up to 10x faster recognition times.
Paper proposes a progressive ensemble network for zero-shot image recognition.
problem Challenges of zero-shot learning due to lack of labeled data and expanding categories.
method Proposes a progressive ensemble network with multiple projected label embeddings.
result Demonstrates improved zero-shot image recognition performance on multiple datasets.
NeuroNER simplifies ANN-based NER for non-experts.
problem Challenging use of ANNs for NER by non-experts.
method Graphical web-based user interface for easy annotation, training, and prediction of entities.
result NeuroNER streamlines NER process for non-expert users.