An Edge-Optimized Incremental Learning Algorithm For Audio Classification

Tsung Han Tsai, Muhammad Awais Hussain, Chun Lin Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In the proposed demo, we would like to show the incremental learning for audio classification using an embedded system. Figure 1 shows the overview of the data processing based on our proposed incremental learning algorithm. The proposed incremental learning algorithm can increase the capability of DNN model to classify new audio sounds which are not included in the base model. In the proposed system, the audio data is gathered at the edge device, and DNN model is trained using our proposed algorithm to learn about the new classes while retaining the knowledge about previous classes as well.

Original languageEnglish
Title of host publicationProceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages504
Number of pages1
ISBN (Electronic)9781665409964
DOIs
StatePublished - 2022
Event4th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022 - Incheon, Korea, Republic of
Duration: 13 Jun 202215 Jun 2022

Publication series

NameProceeding - IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022

Conference

Conference4th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022
Country/TerritoryKorea, Republic of
CityIncheon
Period13/06/2215/06/22

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