Human Age Estimation Through Audio Utilising MFCC and RNN

Authors

  • Ken Ken Universitas Mikroskil
  • Osfredo Quinn Universitas Mikroskil
  • Irpan Adiputra Pardosi Universitas Mikroskil
  • Wenripin Chandra Universitas Pelita Harapan

DOI:

10.33395/sinkron.v8i3.12656

Keywords:

Classification, Age Estimation, Audio, MFCC, RNN

Abstract

Age is one of human main attributes. Age is important factor to improve communication experience. Age estimation has been used in several applications to improve user experience. Therefore, an approach is needed to estimate the user age, one of which is through audio. In this study, Mel Frequency Cepstrum Coefficients (MFCC) and Recurrent Neural Network (RNN) will be used to estimate age through audio. MFCC is used to get features from audio data, while RNN is used to estimate age. Dataset used here was taken from corpus of user speech data on the Common Voice website. This study shows that MFCC and RNN methods are able to estimate human age through audio with highest accuracy obtained in SimpleRNN is 0.5647, and 0.7087 in LSTM.

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How to Cite

Ken Ken, Quinn, O., Pardosi, I. A., & Chandra, W. (2023). Human Age Estimation Through Audio Utilising MFCC and RNN. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(3), 1852-1862. https://doi.org/10.33395/sinkron.v8i3.12656

Issue

Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023

Section

Articles

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Copyright (c) 2023 Wenripin Chandra, Ken Ken, Osfredo Quinn, Irpan Adiputra Pardosi

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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