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Hearing

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When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.

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Related Experiment Video

Updated: May 5, 2026

Infant Auditory Processing and Event-related Brain Oscillations

Infant Auditory Processing and Event-related Brain Oscillations

Published on: July 1, 2015

Audio-based event detection in the operating room.

Jonas Fuchtmann^1,2, Thomas Riedel³, Maximilian Berlet^3,4

¹Research Group MITI, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany. jonas.fuchtmann@tum.de.

International Journal of Computer Assisted Radiology and Surgery

|June 11, 2024

Summary

This summary is machine-generated.

Audio event detection in operating rooms shows high accuracy using just two microphones. This approach offers a cost-effective method for enhancing workflow recognition beyond spoken language analysis.

Keywords:

Audio signal classification Event detection Surgical sound analysis Workflow analysis

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Area of Science:

Medical Informatics
Signal Processing
Machine Learning

Background:

Current operating room (OR) workflow analysis systems are primarily research-based and lack universal application.
The potential of audio data for OR workflow analysis remains largely unexplored, despite advantages like low cost and independence from visual or location data.

Purpose of the Study:

To develop and evaluate an audio-based event detection system for operating rooms.
To explore the feasibility of using sound analysis for comprehensive workflow recognition in surgical environments.

Main Methods:

Utilized a novel dataset of over 63 hours of annotated audio from an operating room.
Processed audio data into log-mel-spectrograms for input into pretrained convolutional neural networks.
Employed data augmentation techniques to improve classification accuracy.

Main Results:

Achieved high performance with lightweight models like MobileNet, demonstrating computational efficiency.
Successfully classified 11 distinct audio event classes, including surgical procedures and equipment movements.
Attained an overall accuracy of 90%, precision of 91%, and F1-score of 91% on the custom dataset.

Conclusions:

Audio event recognition is a viable and valuable addition to OR workflow analysis, extending beyond speech.
The proposed method is computationally inexpensive and can be integrated into existing or future recognition pipelines.
This proof of concept highlights audio as a significant, underutilized data source for surgical environments.