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Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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Classification of Speaking and Singing Voices Using Bioimpedance Measurements and Deep Learning.

Eugenio Donati1, Christos Chousidis2, Henrique De Melo Ribeiro1

  • 1School of Computing and Engineering, University of West London, London, UK.

Journal of Voice : Official Journal of the Voice Foundation
|May 8, 2023
PubMed
Summary

This study introduces a novel deep learning method for distinguishing speaking and singing voices using bioimpedance measurements instead of audio. This approach achieves high accuracy with lower computational cost, enabling real-time voice act classification.

Keywords:
Bioimpedance measurementsEGG-to-MIDIElectroglottographyReal-time voice classificationSinging detectionSpeech classificationVoice information retrievalVoice-to-MIDI

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Distinguishing between speaking and singing voice acts traditionally relies on audio recordings, which can be computationally intensive.
  • The complexity of voice signals presents challenges for accurate and efficient classification.
  • Developing real-time voice act classification systems is crucial for applications like voice-to-MIDI conversion.

Purpose of the Study:

  • To develop a deep learning classifier for differentiating speaking and singing voices using bioimpedance measurements.
  • To replace computationally expensive audio recordings with a more efficient bioimpedance-based approach.
  • To enable real-time voice act classification for integration with other voice processing technologies.

Main Methods:

  • Implementation of a deep neural network classifier utilizing electroglottographic (bioimpedance) signals.
  • Feature extraction using Mel Frequency Cepstral Coefficients (MFCCs).
  • Creation of a dedicated dataset comprising 7200 bioimpedance measurements for speaking and singing.

Main Results:

  • Achieved high classification accuracy (92%–94%) using bioimpedance measurements.
  • Demonstrated significantly lower computational needs for preprocessing and classification compared to audio-based methods.
  • Validated the system's effectiveness through broad testing after model training.

Conclusions:

  • Bioimpedance measurements offer a computationally efficient and accurate alternative for voice act classification.
  • The developed system supports near-real-time applications, including voice-to-MIDI conversion.
  • This research paves the way for more accessible and efficient voice analysis technologies.