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With the rapid development of network infrastructures and personal electronic devices, big data generated from Internet, sensing networks, and other equipment are rapidly growing, and have received increased attention in recent years. Big data consists of multisource content, for example, images, videos, audio, text, spatio-temporal data, and wireless communication data. Moreover, big data processing includes computer vision, natural language processing (NLP), social computing, speech recognition, data analysis in Internet of Vehicle (IoV), real-time data analysis in Internet of Things (IoT), and wireless big data processing.
Recently, artificial intelligence (AI)-driven big data processing technologies based on pattern recognition, machine learning, and deep learning, are intensively applied to deal with the large-scale heterogeneous data. However, challenges still exist in the development of AI-driven big data processing.
In computer vision and image processing, increasingly more databases and data streams have been transmitted and collected. One of the biggest challenges in the massive image/video data analysis is to develop energy efficient and real-time methods to extract useful information out of the colossal amount of data being generated every second. In speech signal processing, benefitting from the help of ‘Big Data’ and new AI technology, a lot of progress has also been made in speech processing area. How to build a condition robustness speech processing system using limited labeled data is still a direction to emphasize studying in the future.