Enhancing smart home environments: a novel pattern recognition approach to ambient acoustic event detection and localization

Ahsan Shabbir ¹, Abdul Haleem Butt ¹, Taha Khan ²

⁰Department of Creative Technologies, Faculty of Computing and Artificial Intelligence, Air University, Islamabad, Pakistan.

Frontiers in Big Data +

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February 7, 2025

View abstract on PubMed

Summary

This study developed an acoustic detection and localization framework for home environments to identify emergency events. The system achieved high accuracy in detecting events and pinpointing sound sources, enabling real-time alerts.

Area Of Science

Artificial Intelligence
Signal Processing
Acoustics

Background

Ambient acoustic event detection and localization are crucial for identifying event origins in acoustic data.
Classifying home activities aids in detecting emergencies and transmitting signals.
Localization improves emergency service effectiveness and response times.

Purpose Of The Study

To establish a comprehensive framework for classifying activities in home environments for emergency event detection.
To enhance situational awareness and emergency response times through acoustic localization.
To develop a real-time emergency alert system for domestic settings.

Main Methods

Collected acoustic data from a home environment using six microphones across various rooms.
Processed 512 audio samples from 11 activities, filtering background noise.
Extracted features from time, frequency, time-frequency, and cepstral domains for detection and localization.
Utilized Random Forest and Linear Discriminant Analysis for event detection and the ESPRIT algorithm for sound source localization.

Main Results

Achieved high detection accuracy: 95% with Random Forest and 87% with Linear Discriminant Analysis.
Demonstrated significant sound source localization performance with an error rate of 3.61.
Reported a Mean Squared Error (MSE) of 14.98 and Root Mean Squared Error (RMSE) of 3.87 for localization.

Conclusions

The integrated detection and localization models successfully identified emergency activities and transmitted notifications.
The proposed methodology shows potential for developing a real-time domestic emergency alert system.
This approach enhances emergency response capabilities in home environments through advanced acoustic analysis.

Keywords:

ESPRIT ambient acoustic analysis autonomous monitoring deep learning machine learning sound event detection sound source localization