Adaptive filtering algorithms are pervasive throughout signal processing and have had a material impact on a wide variety of domains including audio processing, telecommunications, biomedical sensing, astrophysics and cosmology, seismology, and many more. Adaptive filters typically operate via specialized online, iterative optimization methods such as least-mean squares or recursive least squares and aim to process signals in unknown or nonstationary environments. Such algorithms, however, can be slow and laborious to develop, require domain expertise to create, and necessitate mathematical insight for improvement. In this work, we seek to improve upon hand-derived adaptive filter algorithms and present a comprehensive framework for learning online, adaptive signal processing algorithms or update rules directly from data. To do so, we frame the development of adaptive filters as a meta-learning problem in the context of deep learning and use a form of self-supervision to learn online iterative update rules for adaptive filters. To demonstrate our approach, we focus on audio applications and systematically develop meta-learned adaptive filters for five canonical audio problems including system identification, acoustic echo cancellation, blind equalization, multi-channel dereverberation, and beamforming. We compare our approach against common baselines and/or recent state-of-the-art methods. We show we can learn high-performing adaptive filters that operate in real-time and, in most cases, significantly outperform each method we compare against -- all using a single general-purpose configuration of our approach.
翻译:在这项工作中,我们力求改进手取的适应性过滤算法,并为在线学习、适应性信号处理算法或直接从数据中更新规则提供一个综合框架。为了做到这一点,我们将开发适应性过滤器作为深层次学习的元学习问题,并使用一种自我监督的观察形式来学习适应性过滤器的在线迭代更新规则。为了展示我们的方法,我们侧重于音频应用,系统开发元感适应性过滤器,系统开发五种听觉问题,包括系统识别系统、声频取消、盲平准性、多声波处理算法和从数据中直接更新规则。