<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The paper proposes a framework to record meeting to avoid hassle of writing points of meeting. Key components of framework are “Model Trainer” and “Meeting Recorder”. In model trainer, we first clean the noise in audio, then oversample the data size and extract features from audio, in the end we train the classification model. Meeting recorder is a post-processor used for sound recognition using the trained model and converting the audio into text. Experimental results show the high accuracy and effectiveness of the proposed implementation.</p>
</abstract>

The paper proposes a framework to record meeting to avoid hassle of writing points of meeting. Key components of framework are “Model Trainer” and “Meeting Recorder”. In model trainer, we first clean the noise in audio, then oversample the data size and extract features from audio, in the end we train the classification model. Meeting recorder is a post-processor used for sound recognition using the trained model and converting the audio into text. Experimental results show the high accuracy and effectiveness of the proposed implementation.

An Intelligent Framework for Person Identification Using Voice Recognition and Audio Data Classification

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 International License.

The paper proposes a framework to record meeting to avoid hassle of writing points of meeting. Key components of framework are “Model Trainer” and “Meeting...

An Intelligent Framework for Person Identification Using Voice Recognition and Audio Data Classification

Publié en ligne: 24 janv. 2023

Pages: 183 - 189

DOI: https://doi.org/10.2478/acss-2022-0019

Mots clés
Discrete wavelet transform, feature extraction, MFCC, oversampling

© 2022 Isra Khan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

An Intelligent Framework for Person Identification Using Voice Recognition and Audio Data Classification

Publié en ligne: 24 janv. 2023

Pages: 183 - 189

DOI: https://doi.org/10.2478/acss-2022-0019

Mots clésDiscrete wavelet transform, feature extraction, MFCC, oversampling

© 2022 Isra Khan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Discrete wavelet transform, feature extraction, MFCC, oversampling