Respiratory Sound Classification Using Long-Short Term Memory

08/06/2020
by   Chelsea Villanueva, et al.
0

Developing a reliable sound detection and recognition system offers many benefits and has many useful applications in different industries. This paper examines the difficulties that exist when attempting to perform sound classification as it relates to respiratory disease classification. Some methods which have been employed such as independent component analysis and blind source separation are examined. Finally, an examination on the use of deep learning and long short-term memory networks is performed in order to identify how such a task can be implemented.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/10/2019

Transportation Mode Classification from Smartphone Sensors via a Long-Short-Term-Memory Network

This article introduces the architecture of a Long-Short-Term Memory net...
research
06/29/2017

Toward Inverse Control of Physics-Based Sound Synthesis

Long Short-Term Memory networks (LSTMs) can be trained to realize invers...
research
02/08/2023

Short-Term Memory Convolutions

The real-time processing of time series signals is a critical issue for ...
research
12/30/2020

Damaged Fingerprint Recognition by Convolutional Long Short-Term Memory Networks for Forensic Purposes

Fingerprint recognition is often a game-changing step in establishing ev...
research
04/06/2019

Parallelizable Stack Long Short-Term Memory

Stack Long Short-Term Memory (StackLSTM) is useful for various applicati...
research
09/26/2022

Myopia prediction for adolescents via time-aware deep learning

Background: Quantitative prediction of the adolescents' spherical equiva...

Please sign up or login with your details

Forgot password? Click here to reset