机器对环境声音的认知能力是类脑智能研究的重要方向之一。作为机器环境声音认知的一个重要方面，声学事件检测与音频场景识别受到了越来越多的重视。然而，现实中复杂的声学环境给声学事件检测与音频场景识别带来了新的挑战。与此同时，近年来信号处理与机器学习领域中理论与技术的长足发展，也为复杂声学环境下声学事件检测与音频场景识别的研究带来了新的机遇。本项目正是在这样的背景下提出的。项目拟从音频信号的降噪、特征选择与降维、基于机器学习的声学事件检测与音频场景识别方法等方面展开基础性研究。通过项目的研究提出若干具有自主知识产权的理论与技术，为提高机器对环境声音的认知能力提供理论基础和实用方法，促进类脑听觉认知学科的进步。

The cognitive ability of the computer for understanding the environmental sounds is one of the most important research directions in the brain-inspired intelligence. As one of the main aspects of the computer cognition of environmental sounds, the acoustic events detection and audio scenes recognition have been attracted more and more attention. However, there are new challenges for the acoustic events detection and audio scenes recognition in complex acoustic environments. Meanwhile there is also a new opportunity for the acoustic events detection and audio scenes recognition as the fast developments in the theories and technologies of the signal processing and machine learning. Based on the above background, this project is proposed and focuses on the fundamental researches in the denosing of audio signal, the feature selection and dimensionality reduction, and the new machine learning based methods in the acoustic events detection and audio scenes recognition. The main purpose of the project is to propose some theories and technologies with independent intellectual property rights and provide the theoretical principles and useful methods for improving the computer cognition of environmental sounds, and therefore make the progress of the field of the brain-inspired auditory perception.