Resonix - Environmental Sound Classification and Haptic Alert System for Hearing-Impaired Assistance

Hearing-impaired individuals often face difficulty in recognizing important environmental sounds such as sirens, alarms, and warning signals, which can affect their safety and situational awareness in daily life. This paper presents Resonix, an AI-based wearable assistive system designed to identify critical environmental sounds and provide real-time haptic alerts through vibration patterns. Environmental sound datasets including ESC-50 and UrbanSound8K were utilized for model development. Five relevant sound classes — siren, dog bark, alarm, baby cry, and glass break — were selected for classification. Audio preprocessing techniques such as normalization and noise reduction were performed before training the models. Multiple machine learning models including KNN, SVM, Random Forest, and a Convolutional Neural Network (CNN) were evaluated for environmental sound classification. Experimental results showed that the proposed CNN model achieved the highest accuracy of 93.0% compared to other models. The trained model was integrated with an ESP32-based wearable setup consisting of three microphone modules, two vibration motors, and an emergency push button for real-time haptic alert generation. The proposed system aims to improve environmental awareness and assist hearing-impaired individuals through a simple and low-cost wearable solution.

Convolutional Neural Network (CNN), Wearable Assistive System, Haptic Alert System, ESP32, Machine Learning, Audio Classification

1. K. J. Piczak, “ESC: Dataset for Environmental Sound Classification,” Proceedings of ACM Multimedia, 2015. [Google Scholar] [Crossref]

2. G. Park and S. Lee, “Environmental Noise Classification Using Convolutional Neural Networks,” Sensors, vol. 20, no. 8, 2020. [Google Scholar] [Crossref]

3. W. Mu et al., “Environmental sound classification using temporal-frequency attention based CNN,” Scientific Reports, 2021. [Google Scholar] [Crossref]

4. Y. Su et al., “Environment Sound Classification Using a Two-Stream CNN,” Sensors, 2019. [Google Scholar] [Crossref]

5. A. Bansal et al., “Environmental Sound Classification: A descriptive review,” Intelligent Systems with Applications, 2022. [Google Scholar] [Crossref]

6. Z. Mushtaq et al., “Spectral images based environmental sound classification using CNN,” Applied Acoustics, 2021. [Google Scholar] [Crossref]

7. J. Guo et al., “A Deep Attention Model for Environmental Sound Classification,” Applied Sciences, 2022. [Google Scholar] [Crossref]

8. R. Akter et al., “A hybrid CNN-LSTM model for environmental sound classification,” Digital Signal Processing, 2025. [Google Scholar] [Crossref]

9. S. Abdoli et al., “End-to-End Environmental Sound Classification using 1D CNN,” arXiv, 2019. [Google Scholar] [Crossref]

10. J. Sharma et al., “Environment Sound Classification using Multiple Feature Channels and Attention-based DCNN,” arXiv, 2019. [Google Scholar] [Crossref]

11. B. Zhu et al., “Environmental Sound Classification Based on Multi-temporal Resolution CNN,” arXiv, 2018. [Google Scholar] [Crossref]

12. D. Jain et al., “SoundWatch: Exploring Smartwatch-based Deep Learning for Sound Accessibility,” ASSETS, 2020. [Google Scholar] [Crossref]

13. D. Jain et al., “Deep Learning for Sound Accessibility on Smartwatches,” Communications of the ACM, 2022. [Google Scholar] [Crossref]

14. A. Ashurov et al., “Environmental Sound Classification Based on Transfer Learning,” Electronics, 2022. [Google Scholar] [Crossref]

15. Z. Zhang et al., “Deep CNN with Mixup for Environmental Sound Classification,” arXiv, 2018. [Google Scholar] [Crossref]

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