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Sound Intensity Level00:53

Sound Intensity Level

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Humans perceive sound by hearing. The human ear helps sound waves reach the brain, which then interprets the waves and creates the perception of hearing. The loudness of the environment in which a person is located determines whether they can distinguish between different sound sources.
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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Sound Intensity00:58

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The loudness of a sound source is related to how energetically the source is vibrating, consequently making the molecules of the propagation medium vibrate. To measure the loudness of a source, the physical quantity of interest is the intensity. This is defined as the energy emitted per unit of time per unit of area perpendicular to the sound wave's propagation direction. Since the total energy is greater if the source vibrates for a longer duration and over a larger area, dividing the...
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Pain Level Classification from Speech Using GRU-Mixer Architecture with Log-Mel Spectrogram Features.

Adi Alhudhaif1

  • 1Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam Bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|September 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces the Gated Recurrent Unit (GRU)-Mixer, a deep learning model for automatic pain detection from speech. It achieves high accuracy in classifying pain levels, offering a promising tool for non-invasive patient assessment.

Keywords:
GRU-MixerLog-Mel spectrogramspain level determinationspeech signals

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Area of Science:

  • Computational linguistics
  • Affective computing
  • Machine learning for healthcare

Background:

  • Automatic pain detection from speech offers non-invasive, real-time assessment, crucial for patients unable to self-report.
  • Existing methods require further development for robust clinical application.

Purpose of the Study:

  • To introduce and evaluate the Gated Recurrent Unit (GRU)-Mixer, a novel deep learning model for speech-based pain classification.
  • To establish a benchmark for future research in affective computing and pain recognition.

Main Methods:

  • A lightweight recurrent deep learning model (GRU-Mixer) was developed, processing Log-Mel spectrograms from speech.
  • The model utilizes stacked bidirectional GRUs and adaptive average pooling for temporal feature extraction.
  • Speaker-independent training with class-balanced loss was employed for generalization across binary, graded intensity, and thermal-state pain classification tasks.

Main Results:

  • The GRU-Mixer achieved 83.86% accuracy for binary pain detection (pain vs. non-pain).
  • Multiclass pain intensity classification (mild, moderate, severe) reached 75.36% accuracy.
  • The model demonstrated strong performance on the TAME Pain dataset.

Conclusions:

  • The GRU-Mixer presents an effective benchmark architecture for speech-based pain recognition.
  • This study is the first deep learning classification work on the TAME Pain dataset.
  • The findings support the potential of AI in objective pain assessment through vocal biomarkers.