声学中的机器学习：理论与应用
Machine learning in acoustics: theory and applications
摘要：
声学数据提供了从生物学和通信到海洋和地球科学等领域的科学和工程见解。我们调查了机器学习（ML）在声学领域的最新进展和变革潜力，包括深度学习。ML是一系列广泛的技术，通常基于统计学，用于自动检测和利用数据中的模式。相对于传统的声学和信号处理，ML是数据驱动的。如果有足够的训练数据，机器学习可以发现特征与所需标签或动作之间的复杂关系，或者特征本身之间的复杂关系。通过大量的训练数据，ML可以发现描述复杂声学现象的模型，例如人类语音和混响。声学中的ML正在迅速发展，具有令人信服的结果和重大的未来前景。我们首先介绍ML，然后突出ML在四个声学研究领域的发展：语音处理中的源定位，海洋声学中的源定位，生物声学和日常场景中的环境声音。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
