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Li, J., Dai, W., Metze, F., Qu, S. and Das, S. (2017) A Comparison of Deep Learning Methods for Environmental Sound Detection. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, 5-9 March 2017, 126-130.
https://doi.org/10.1109/icassp.2017.7952131

has been cited by the following article:

• TITLE: Addressing Hearing Impairments through Machine Learning: A Review of Sound Detection and Assistive Technologies

  AUTHORS: Nada Barnawi, Mohammed Alnuem

  KEYWORDS: Machine Learning-Based, Deaf and Hard of Hearing, Sound Detection, Assistive Technologies, Speech Recognition, Realtime

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.15 No.8, August 21, 2025

  ABSTRACT: This paper studies recent assistive technologies and AI sound detection systems that have been developed to support both the safety and communication of individuals who are deaf. It highlights how modern sound detection systems effectively address challenges such as real-time processing and polyphonic audio environments, while integrating speech recognition to improve situational awareness and interaction. The findings confirm that these technologies not only increase auditory accessibility but also empower greater independence and security for deaf and hard-of-hearing users in everyday environments.