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首页> 外文期刊>IEEE Spectrum >Deep learning reinvents the hearing aid
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Deep learning reinvents the hearing aid

机译:深度学习彻底改造了助听器

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摘要

My mother began to lose her hearing while I was away at college. I would return home to share what I'd learned, and she would lean in to hear. Soon it became difficult for her to hold a conversation if more than one person spoke at a time. Now, even with a hearing aid, she struggles to distinguish the sounds of each voice. When my family visits for dinner, she still pleads with us to speak in turn. My mother's hardship reflects a classic problem for hearing aid manufacturers. The human auditory system can naturally pick out a voice in a crowded room, but creating a hearing aid that mimics that ability has stumped signal processing specialists, artificial intelligence experts, and audiologists for decades. British cognitive scientist Colin Cherry first dubbed this the "cocktail party problem" in 1953.
机译:我上大学时,母亲开始失去听力。我会回到家分享我学到的东西,她会倾听。很快,如果一次不只一个人讲话,她就很难进行对话。现在,即使有了助听器,她也很难分辨每个声音的声音。当我的家人探望晚餐时,她仍然恳求我们轮流发言。我母亲的艰辛反映了助听器制造商面临的一个典型问题。人工听觉系统可以自然地在拥挤的房间中拾取声音,但是创建模仿这种能力的助听器困扰了信号处理专家,人工智能专家和听力学家数十年。英国认知科学家科林·切里(Colin Cherry)于1953年首次将其称为“鸡尾酒会问题”。

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