{"title":"基于神经网络客观测量的听力损失补偿技术","authors":"A. Tungthangthum, J. C. Rutledge","doi":"10.1109/ASPAA.1993.379988","DOIUrl":null,"url":null,"abstract":"An objective measures system has been developed to predict the results of subject-based tests for sensorineural hearing loss compensation techniques. Parameters related to the loudness level of the compensated speech signal are extracted from its frequency spectrum. These parameters are then used to train a neural network based phoneme classifier. Good prediction results have been achieved for two hearing impaired subjects.<<ETX>>","PeriodicalId":270576,"journal":{"name":"Proceedings of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1993-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Objective measures based on neural networks for hearing loss compensation techniques\",\"authors\":\"A. Tungthangthum, J. C. Rutledge\",\"doi\":\"10.1109/ASPAA.1993.379988\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An objective measures system has been developed to predict the results of subject-based tests for sensorineural hearing loss compensation techniques. Parameters related to the loudness level of the compensated speech signal are extracted from its frequency spectrum. These parameters are then used to train a neural network based phoneme classifier. Good prediction results have been achieved for two hearing impaired subjects.<<ETX>>\",\"PeriodicalId\":270576,\"journal\":{\"name\":\"Proceedings of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics\",\"volume\":\"54 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1993-10-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASPAA.1993.379988\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASPAA.1993.379988","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Objective measures based on neural networks for hearing loss compensation techniques
An objective measures system has been developed to predict the results of subject-based tests for sensorineural hearing loss compensation techniques. Parameters related to the loudness level of the compensated speech signal are extracted from its frequency spectrum. These parameters are then used to train a neural network based phoneme classifier. Good prediction results have been achieved for two hearing impaired subjects.<>
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