{"title":"扩展卡尔曼滤波对声门源波形进行自动低频模型拟合","authors":"Haoxuan Li, Ronan Scaife, Darragh O'Brien","doi":"10.5281/ZENODO.42972","DOIUrl":null,"url":null,"abstract":"A new method for automatically fitting the Liljencrants-Fant (LF) model to the time domain waveform of the glottal flow derivative is presented in this paper. By applying an extended Kalman filter (EKF) to track the LF-model shape-controlling parameters and dynamically searching for a globally minimal fitting error, the algorithm can accurately fit the LF-model to the inverse filtered glottal flow derivative. Experimental results show that the method has better performance for both synthetic and real speech signals compared to a standard time-domain LF-model fitting algorithm. By offering a new method to estimate the glottal source LF-model parameters, the proposed algorithm can be utilised in many applications.","PeriodicalId":201182,"journal":{"name":"2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Automatic LF-model fitting to the glottal source waveform by extended Kalman filtering\",\"authors\":\"Haoxuan Li, Ronan Scaife, Darragh O'Brien\",\"doi\":\"10.5281/ZENODO.42972\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new method for automatically fitting the Liljencrants-Fant (LF) model to the time domain waveform of the glottal flow derivative is presented in this paper. By applying an extended Kalman filter (EKF) to track the LF-model shape-controlling parameters and dynamically searching for a globally minimal fitting error, the algorithm can accurately fit the LF-model to the inverse filtered glottal flow derivative. Experimental results show that the method has better performance for both synthetic and real speech signals compared to a standard time-domain LF-model fitting algorithm. By offering a new method to estimate the glottal source LF-model parameters, the proposed algorithm can be utilised in many applications.\",\"PeriodicalId\":201182,\"journal\":{\"name\":\"2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5281/ZENODO.42972\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5281/ZENODO.42972","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
摘要
本文提出了一种将lijencrants - fant (LF)模型与声门流导数的时域波形自动拟合的新方法。该算法采用扩展卡尔曼滤波器(EKF)跟踪声门流模型的形控参数,并动态搜索全局最小拟合误差,将声门流模型精确拟合到反滤波的声门流导数上。实验结果表明,与标准时域lf模型拟合算法相比,该方法对合成语音信号和真实语音信号都具有更好的性能。该算法提供了一种估计声门源低频模型参数的新方法,可用于多种应用场合。
Automatic LF-model fitting to the glottal source waveform by extended Kalman filtering
A new method for automatically fitting the Liljencrants-Fant (LF) model to the time domain waveform of the glottal flow derivative is presented in this paper. By applying an extended Kalman filter (EKF) to track the LF-model shape-controlling parameters and dynamically searching for a globally minimal fitting error, the algorithm can accurately fit the LF-model to the inverse filtered glottal flow derivative. Experimental results show that the method has better performance for both synthetic and real speech signals compared to a standard time-domain LF-model fitting algorithm. By offering a new method to estimate the glottal source LF-model parameters, the proposed algorithm can be utilised in many applications.