混响
计算机科学
声学
转化式学习
领域(数学)
水声学
信号(编程语言)
信号处理
测距
语音识别
人工智能
数据科学
地质学
数学
水下
物理
电信
心理学
海洋学
程序设计语言
纯数学
雷达
教育学
作者
Michael J. Bianco,Peter Gerstoft,James Traer,Emma Ozanich,Marie A. Roch,Sharon Gannot,Charles‐Alban Deledalle,Weichang Li
出处
期刊:Cornell University - arXiv
日期:2019-05-11
被引量:6
摘要
Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics. ML is a broad family of techniques, which are often based in statistics, for automatically detecting and utilizing patterns in data. Relative to conventional acoustics and signal processing, ML is data-driven. Given sufficient training data, ML can discover complex relationships between features and desired labels or actions, or between features themselves. With large volumes of training data, ML can discover models describing complex acoustic phenomena such as human speech and reverberation. ML in acoustics is rapidly developing with compelling results and significant future promise. We first introduce ML, then highlight ML developments in four acoustics research areas: source localization in speech processing, source localization in ocean acoustics, bioacoustics, and environmental sounds in everyday scenes.
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