Footstep classification using simple speech recognition technique
Footstep classification using simple speech recognition technique
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DOI：
10.1109/iscas.2008.4542147
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发表时间：
2008-05
期刊：
影响因子：
--
通讯作者：
A. Itai;H. Yasukawa
中科院分区：
文献类型：
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作者：
A. Itai;H. Yasukawa
人类脚步的特征是由步态、鞋子和地板决定的。准确的脚步分析将在各种应用，家庭安全服务，监控和理解人类行为中发挥作用，因为步态表达了个性，年龄和性别。利用声学特征参数[1]进行脚步声分类的可行性已经得到了证实，然而，传统的方法几乎都集中在统计特征和模式识别上。在语音识别中，特征字符串在时域内被拉伸和压缩。动态规划是一种有效的吸收时域波动的方法。在脚步声分类中，脚步声声（即撞击声和摩擦声）在时间域上的扩张和收缩与讲话相同。本文将DTW和倒位法应用于足迹分类。结果表明，所提出的方法可用于足迹识别问题。
The characteristics of human footsteps are determined by the gait, the footwear and the floor. Accurate footstep analysis would be useful in various applications, home security service, surveillance and understanding of human action since the gait expresses personality, age and gender. The feasibility of a footstep classification has been confirmed by using the acoustic feature parameter[1], however, almost of conventional approaches are focused on the statistical features and pattern recognitions. In the speech recognition, a feature string is stretched and compressed in the time domain. The dynamic programming is used to accomplish this task, and is an effective method of absorbing time domain fluctuations. In the footstep classification, footstep sound (i.e. an impact sound and a fricative sound) is expanded and contracted in the time domain the same as speeches. This paper applies the DTW and cepstra to the footstep classification. Result shows that the proposed method is useful to the footstep recognition problems.