一种基于背景声音识别的社会情境感知方法

随着社会需求的不断扩大及技术的不断发展，人与人之间的社会交互也越来越多.理解社会交互特征并能感知用户所处的社会情境语义（如在开会、在上课），对于促进和辅助用户社会活动具有重要意义.从背景声音的角度对社会交互进行理解，目的是通过对背景声音差异性特征的提取，识别用户所处的社会情境.提出了一种基于背景声音识别的社会情境感知方法，该方法采用Mel frequency cepstral coefficients （MFCCs，即Mel 频率倒谱系数）分析声音信号，将路径搜索限制和搜索过滤的改进Dynamic Time Warping（DTW）算法作为识别器.通过对11 种社会情境背景声音的采集和识别，表明该算法能够有效地识别用户所处的社会情境，且其运算效率与识别率比传统DTW 算法有所提高.

Approach of Social Context Awareness Based on Background Sound Recognition

With the spread of social needs and development of techniques, social interaction is more and more frequent among people. To promote and assist human social interaction, it's important to understand the social context the user situates. The paper mainly studies the understanding of social contexts based on background sounds, the goal of which is to recognize the social context in which users reside through analyzing the differences of background sounds. It uses the Mel frequency cepstral coefficients to analyze sound features and classify the sounds based on an improved Dynamic Time Warping (DTW) algorithm. Experimental results show that the proposed algorithm is more effective than traditional methods.