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首页> 外文会议>Conference on Speech Technology and Human-Computer Dialogue >SOUND CLASSIFICATION IN A SMART ROOM ENVIRONMENT: AN APPROACH USING GMM AND HMM METHODS
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SOUND CLASSIFICATION IN A SMART ROOM ENVIRONMENT: AN APPROACH USING GMM AND HMM METHODS

机译：智能房间环境中的声音分类：使用GMM和HMM方法的方法

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Because of cost or convenience reasons, patients or elderly people would be hospitalized at home and smart information systems would be needed in order to assist human operators. In this case, position and physiologic sensors give already numerous informations, but there are few studies for sound use in patient's habitation. However, sound classification and speech recognition may greatly increase the versatility of such a system: this will be provided by detecting short sentences or words that could characterize a distress situation for the patient. Analysis and classification of sounds emitted in patient's habitation may be useful for patient's activity monitoring. GMMs and HMMs are well suited for sound classification. Until now, GMMs are frequently used for sound classification in smart rooms because of their low computational costs, but HMMs should allow a finer analysis: indeed the use of 3 states HMMs should allow better performances by taking into account the variation of the signal according to time. For this framework a new sound corpus was recorded in experimental conditions. This corpus includes 8 sound classes useful for our application. The choice of needed acoustical features and the two approaches are presented. Then an evaluation is made with the initial corpus and with additional experimental noise. The obtained results are compared. At the end of this framework a segmentation module is presented. This module has the ability of extracting isolated sounds in a record by the means of a wavelet filtering method which allows the extraction in noisy conditions.
机译：由于成本或便利原因，患者或老年人将在家庭住院，并且需要智能信息系统以协助人类运营商。在这种情况下，位置和生理传感器已经提供了众多信息，但很少有患者居住的声音使用的研究。然而，声音分类和语音识别可能会大大增加这种系统的多功能性：这将通过检测可以表征患者遇到痛苦情况的短句或单词来提供。患者居住地发出的​​声音分析和分类对于患者的活动监测可能有用。 GMMS和HMMS非常适合进行声音分类。到目前为止，GMMS经常用于智能房间的声音分类，因为它们的计算成本低，但HMMS应允许更精细的分析：确实使用3个州HMMS根据信号的变化应该更好地表现。时间。对于此框架，在实验条件下记录了新的声音语料库。此语料库包括8个用于我们应用的声课。提供所需的声学特征和两种方法的选择。然后用初始语料库和额外的实验噪声进行评估。比较了得到的结果。在本框架的末尾，呈现了分段模块。该模块具有通过小波滤波方法的方式提取孤立声音的能力，该方法允许在嘈杂的条件下提取。

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