海报:mobiear——利用深度学习为聋人搭建与环境无关的声音感知平台

Sicong Liu, Junzhao Du
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引用次数: 8

摘要

声波报警器每年都能从火灾、煤气泄漏和漏电事故中拯救成千上万人的生命。通过播放不同音调、响度和音色的声音,声音警报器让人们意识到周围的环境,通知他们意外事件,并通知他们关键信息。然而,对于耳聋或对声信号不太敏感的人来说,通过声报警来保持安全意识是比较困难的。他们往往是最后一批获得重要信息的人,即使他们处于危险之中,尤其是当他们独自一人时。通过利用智能手机上的麦克风,普遍的声音感知应用正在成为可能。深度学习模型在准确性和稳健性方面有很大的飞跃。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Poster: MobiEar-Building an Environment-independent Acoustic Sensing Platform for the Deaf using Deep Learning
Acoustic alarms have been credited with saving thousands of lives from fires, gas leakage and electric leakage each year. By broadcasting sound with different tones, loudness and timbres, acoustic alarms keep people aware of surroundings, inform them of serendipitous events, and notify them critical information. However, maintaining the safety awareness through the acoustic alarm is difficult for people who are deaf or less sensitive to acoustic signals. They are too often among the last to access important information even when they are in dangers, especially when they stay alone. By leveraging the microphone on commodity smartphones, universal sound awareness applications are becoming possible. Deep learning models have large leaps in accuracy and robustness[1].
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