Search Results for author:
Found 10 papers, 1 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.
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.
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.
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.
