{"title":"基于预滤波和加权小波系数的含噪语音基音检测方法","authors":"Ru-wei Li, C. Bao, Hui-jing Dou","doi":"10.1109/ICOSP.2008.4697187","DOIUrl":null,"url":null,"abstract":"Most of the current pitch detection algorithms can not work well under the high noise environment. For this reason, a pitch detection algorithm for noisy speech signal based on pre-filtering and weighted wavelet coefficients is proposed. Firstly, the noisy speech signals are pre-filtered. Secondly, the speech pre-filtered is decomposed by the quadratic spline wavelet. Thirdly, the wavelet coefficients of three consecutive scales are weighted to emphasize the sharp change points. Fourthly, three candidate pitch periods are extracted from the weighted signals. Finally, the pitch period is calculated by autocorrelation function. Experiments show that this algorithm can increase the performance of pitch detection in noisy environment and decreases computational complexity compared with DWT-NCCF method.","PeriodicalId":445699,"journal":{"name":"2008 9th International Conference on Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Pitch detection method for noisy speech signals based on pre-filter and weighted wavelet coefficients\",\"authors\":\"Ru-wei Li, C. Bao, Hui-jing Dou\",\"doi\":\"10.1109/ICOSP.2008.4697187\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Most of the current pitch detection algorithms can not work well under the high noise environment. For this reason, a pitch detection algorithm for noisy speech signal based on pre-filtering and weighted wavelet coefficients is proposed. Firstly, the noisy speech signals are pre-filtered. Secondly, the speech pre-filtered is decomposed by the quadratic spline wavelet. Thirdly, the wavelet coefficients of three consecutive scales are weighted to emphasize the sharp change points. Fourthly, three candidate pitch periods are extracted from the weighted signals. Finally, the pitch period is calculated by autocorrelation function. Experiments show that this algorithm can increase the performance of pitch detection in noisy environment and decreases computational complexity compared with DWT-NCCF method.\",\"PeriodicalId\":445699,\"journal\":{\"name\":\"2008 9th International Conference on Signal Processing\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 9th International Conference on Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOSP.2008.4697187\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 9th International Conference on Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOSP.2008.4697187","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Pitch detection method for noisy speech signals based on pre-filter and weighted wavelet coefficients
Most of the current pitch detection algorithms can not work well under the high noise environment. For this reason, a pitch detection algorithm for noisy speech signal based on pre-filtering and weighted wavelet coefficients is proposed. Firstly, the noisy speech signals are pre-filtered. Secondly, the speech pre-filtered is decomposed by the quadratic spline wavelet. Thirdly, the wavelet coefficients of three consecutive scales are weighted to emphasize the sharp change points. Fourthly, three candidate pitch periods are extracted from the weighted signals. Finally, the pitch period is calculated by autocorrelation function. Experiments show that this algorithm can increase the performance of pitch detection in noisy environment and decreases computational complexity compared with DWT-NCCF method.