In the last decade, the signal processing (SP) community has witnessed a paradigm shift from model-based to data-driven methods. Machine learning (ML)—more specifically, deep learning—methodologies are nowadays widely used in all SP fields, e.g., audio, speech, image, video, multimedia, and multimodal/multisensor processing, to name a few. Many data-driven methods also incorporate domain knowledge to improve problem modeling, especially when computational burden, training data scarceness, and memory size are important constraints.

中文翻译：

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数据科学教育：信号处理视角 [SP 教育]


在过去的十年中，信号处理 (SP) 社区见证了从基于模型的方法到数据驱动的方法的范式转变。机器学习（ML）——更具体地说，深度学习——方法如今广泛应用于所有 SP 领域，例如音频、语音、图像、视频、多媒体和多模式/多传感器处理等。许多数据驱动的方法还结合了领域知识来改进问题建模，特别是当计算负担、训练数据稀缺性和内存大小是重要限制时。