Search Results for author:
Found 25 papers, 2 papers with code
The Impact of Frequency Bands on Acoustic Anomaly Detection of Machines using Deep Learning Based Model
In this paper, we propose a deep learning based model for Acoustic Anomaly Detection of Machines, the task for detecting abnormal machines by analysing the machine sound.
LSTM-based Deep Neural Network With A Focus on Sentence Representation for Sequential Sentence Classification in Medical Scientific Abstracts
For this reason, the role of sentence embedding is crucial for capturing both the semantic information between words in the sentence and the contextual relationship of sentences within the abstract to provide a comprehensive representation for better classification.
Landslide Detection and Segmentation Using Remote Sensing Images and Deep Neural Network
Finally, we propose a combined loss function which leverages Focal loss and IoU loss to train the network.
Robust-MBFD: A Robust Deep Learning System for Motor Bearing Faults Detection Using Multiple Deep Learning Training Strategies and A Novel Double Loss Function
To this end, we first propose and evaluate various machine learning based systems for the MBFD task.
Robust-MBDL: A Robust Multi-branch Deep Learning Based Model for Remaining Useful Life Prediction and Operational Condition Identification of Rotating Machines
In this paper, a Robust Multi-branch Deep learning-based system for remaining useful life (RUL) prediction and condition operations (CO) identification of rotating machines is proposed.
Low-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms
In the second phase, the student network, which presents a low complexity model, is trained with the embeddings extracted from the teacher.
An Inception-Residual-Based Architecture with Multi-Objective Loss for Detecting Respiratory Anomalies
Notably, we achieved the Top-1 performance in Task 2-1 and Task 2-2 with the highest Score of 74. 5% and 53. 9%, respectively.
A Light-weight Deep Learning Model for Remote Sensing Image Classification
In this paper, we present a high-performance and light-weight deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the aerial scene of a remote sensing image.
A Robust and Low Complexity Deep Learning Model for Remote Sensing Image Classification
In this paper, we present a robust and low complexity deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the scene of a remote sensing image.
Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context
In this paper, we present a comprehensive analysis of Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording from its acoustic signature.
Remote Sensing Image Classification using Transfer Learning and Attention Based Deep Neural Network
The task of remote sensing image scene classification (RSISC), which aims at classifying remote sensing images into groups of semantic categories based on their contents, has taken the important role in a wide range of applications such as urban planning, natural hazards detection, environment monitoring, vegetation mapping, or geospatial object detection.
Low-complexity deep learning frameworks for acoustic scene classification
In this report, we presents low-complexity deep learning frameworks for acoustic scene classification (ASC).
Wider or Deeper Neural Network Architecture for Acoustic Scene Classification with Mismatched Recording Devices
In this paper, we present a robust and low complexity system for Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording.
An Audio-Visual Dataset and Deep Learning Frameworks for Crowded Scene Classification
This paper presents a task of audio-visual scene classification (SC) where input videos are classified into one of five real-life crowded scenes: 'Riot', 'Noise-Street', 'Firework-Event', 'Music-Event', and 'Sport-Atmosphere'.
An Analysis of State-of-the-art Activation Functions For Supervised Deep Neural Network
This paper provides an analysis of state-of-the-art activation functions with respect to supervised classification of deep neural network.
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.
Deep Learning Framework Applied for Predicting Anomaly of Respiratory Sounds
This paper proposes a robust deep learning framework used for classifying anomaly of respiratory cycles.
On Multitask Loss Function for Audio Event Detection and Localization
Audio event localization and detection (SELD) have been commonly tackled using multitask models.
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.
Robust Deep Learning Framework For Predicting Respiratory Anomalies and Diseases
This paper presents a robust deep learning framework developed to detect respiratory diseases from recordings of respiratory sounds.
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.
Spatio-Temporal Attention Pooling for Audio Scene Classification
Acoustic scenes are rich and redundant in their content.
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.
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.
