Multi-Microphone Signal Processing and Machine Learning

多麦克风信号处理和机器学习

• 批准号: 516327-2017
• 负责人: Bolic, Miodrag
• 金额: $ 1.82万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=640074
• 关键词: Multi; Microphone; Signal; Processing; Machine

In this project, we will address the problem of improving quality of the audio signals in environments withmultiple microphones and multiple speakers. In future smart houses, it is envisioned that there would be avariety of sensors including many inexpensive microphones connected wirelessly to a central station. Toimprove the quality of audio signals, data from multiple microphones will need to be combined using machinelearning and signal processing algorithms.One of the challenges that we will address in this project is to synchronize audio signals from each microphonewith low latency so that it appears to the user as if there is no delay in processing. Another challenge is to usemultiple microphones to supress noise and amplify the audio signals of interest.This project is beneficial to Canada because it has potential to improve future smart homes. In addition, Canadais investing significantly in machine learning and artificial intelligence, and these methods will be used in thisproject. The supporting company will benefit from the results of this project because the methods developed inthis research could be directly applied to their current products.

在这个项目中，我们将解决在多麦克风和多扬声器的环境中提高音频信号质量的问题。在未来的智能住宅中，可以设想会有各种各样的传感器，包括许多无线连接到中心站的廉价麦克风。为了提高音频信号的质量，需要使用机器学习和信号处理算法将来自多个麦克风的数据组合在一起。我们将在这个项目中解决的挑战之一是以低延迟同步来自每个麦克风的音频信号，以便在用户看来似乎没有处理延迟。另一个挑战是使用多个麦克风来抑制噪音和放大感兴趣的音频信号。这个项目对加拿大有利，因为它有潜力改善未来的智能家居。此外，加拿大在机器学习和人工智能方面投入了大量资金，这些方法将用于该项目。支持公司将受益于本项目的结果，因为在这项研究中开发的方法可以直接应用于他们目前的产品。