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Found 28 papers, 8 papers with code
Equilibrium Model with Anisotropy for Model-Based Reconstruction in Magnetic Particle Imaging
Magnetic particle imaging is a tracer-based tomographic imaging technique that allows the concentration of magnetic nanoparticles to be determined with high spatio-temporal resolution.
L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging
In this work, we show that while encoding the logic of a whole sleep cycle is crucial to improve sleep staging performance, the sequential modelling approach in existing state-of-the-art deep learning models are inefficient for that purpose.
Polyphonic audio event detection: multi-label or multi-class multi-task classification problem?
The network is composed of a backbone subnet and multiple task-specific subnets.
Pediatric Automatic Sleep Staging: A comparative study of state-of-the-art deep learning methods
Background: Despite the tremendous progress recently made towards automatic sleep staging in adults, it is currently unknown if the most advanced algorithms generalize to the pediatric population, which displays distinctive characteristics in overnight polysomnography (PSG).
SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification
It is based on the transformer backbone and offers interpretability of the model's decisions at both the epoch and sequence level.
Multi-view Audio and Music Classification
The learned embedding in the subnetworks are then concatenated to form the multi-view embedding for classification similar to a simple concatenation network.
Inception-Based Network and Multi-Spectrogram Ensemble Applied For Predicting Respiratory Anomalies and Lung Diseases
This paper presents an inception-based deep neural network for detecting lung diseases using respiratory sound input.
Self-Attention Generative Adversarial Network for Speech Enhancement
Existing generative adversarial networks (GANs) for speech enhancement solely rely on the convolution operation, which may obscure temporal dependencies across the sequence input.
On Multitask Loss Function for Audio Event Detection and Localization
Audio event localization and detection (SELD) have been commonly tackled using multitask models.
XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging
This work proposes a sequence-to-sequence sleep staging model, XSleepNet, that is capable of learning a joint representation from both raw signals and time-frequency images.
Ranked #1 on
Sleep Stage Detection
on PhysioNet Challenge 2018
Personalized Automatic Sleep Staging with Single-Night Data: a Pilot Study with KL-Divergence Regularization
We employ the pretrained SeqSleepNet (i. e. the subject independent model) as a starting point and finetune it with the single-night personalization data to derive the personalized model.
CNN-MoE based framework for classification of respiratory anomalies and lung disease detection
This paper presents and explores a robust deep learning framework for auscultation analysis.
Improving GANs for Speech Enhancement
The former constrains the generators to learn a common mapping that is iteratively applied at all enhancement stages and results in a small model footprint.
Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning
We employ the Montreal Archive of Sleep Studies (MASS) database consisting of 200 subjects as the source domain and study deep transfer learning on three different target domains: the Sleep Cassette subset and the Sleep Telemetry subset of the Sleep-EDF Expanded database, and the Surrey-cEEGrid database.
Ranked #1 on
Multimodal Sleep Stage Detection
on Surrey-PSG
Automatic Sleep Stage Classification
Multimodal Sleep Stage Detection
+2
Deep Transfer Learning for Single-Channel Automatic Sleep Staging with Channel Mismatch
This work presents a deep transfer learning approach to overcome the channel mismatch problem and transfer knowledge from a large dataset to a small cohort to study automatic sleep staging with single-channel input.
Spatio-Temporal Attention Pooling for Audio Scene Classification
Acoustic scenes are rich and redundant in their content.
Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene?
Moreover, as model fusion with deep network ensemble is prevalent in audio scene classification, we further study whether, and if so, when model fusion is necessary for this task.
Unifying Isolated and Overlapping Audio Event Detection with Multi-Label Multi-Task Convolutional Recurrent Neural Networks
We propose a multi-label multi-task framework based on a convolutional recurrent neural network to unify detection of isolated and overlapping audio events.
Enabling Early Audio Event Detection with Neural Networks
The proposed system consists of a novel inference step coupled with dual parallel tailored-loss deep neural networks (DNNs).
DNN and CNN with Weighted and Multi-task Loss Functions for Audio Event Detection
Our proposed systems significantly outperform the challenge baseline, improving F-score from 72. 7% to 90. 0% and reducing detection error rate from 0. 53 to 0. 18 on average on the development data.
Audio Scene Classification with Deep Recurrent Neural Networks
We introduce in this work an efficient approach for audio scene classification using deep recurrent neural networks.
Classifying Variable-Length Audio Files with All-Convolutional Networks and Masked Global Pooling
We trained a deep all-convolutional neural network with masked global pooling to perform single-label classification for acoustic scene classification and multi-label classification for domestic audio tagging in the DCASE-2016 contest.
CNN-LTE: a Class of 1-X Pooling Convolutional Neural Networks on Label Tree Embeddings for Audio Scene Recognition
This category taxonomy is then used in the feature extraction step in which an audio scene instance is represented by a label tree embedding image.
CaR-FOREST: Joint Classification-Regression Decision Forests for Overlapping Audio Event Detection
The regression phase is then carried out to let the positive audio segments vote for the event onsets and offsets, and therefore model the temporal structure of audio events.
Label Tree Embeddings for Acoustic Scene Classification
We present in this paper an efficient approach for acoustic scene classification by exploring the structure of class labels.
Learning Compact Structural Representations for Audio Events Using Regressor Banks
The entries of the descriptor are produced by evaluating a set of regressors on the input signal.
Robust Audio Event Recognition with 1-Max Pooling Convolutional Neural Networks
We present in this paper a simple, yet efficient convolutional neural network (CNN) architecture for robust audio event recognition.
Comparing Time and Frequency Domain for Audio Event Recognition Using Deep Learning
Recognizing acoustic events is an intricate problem for a machine and an emerging field of research.
