{"title":"A synthetic sleep snoring study through the use of linear predictive speech techniques","authors":"M. Rezki, M. Ayad","doi":"10.1109/SSD54932.2022.9955803","DOIUrl":null,"url":null,"abstract":"Snoring is a disagreeable sound produced by humans while they sleep and in some dimensions, it is considered pathology. Characterized by inspiratory signals, it is closely related to the breathing function. This paper deals with the sleeping snore using an efficient approach based on the synthesis of a recorded snoring signal. The advantages of this approach are very varied such as offers of a non-contact substitute, artificial reproduction by machine of the original signal (snoring), which can even be integrated later in humanoid robots as an example. The method itself of this reconstitution is a reproduce of the signal through the application of some predictive techniques such as LPC (linear predictive coding), and CELP (Code-excited linear prediction). The difference between original and synthetic signals, called also residuals, can be explained by a scanning factor and different types of noises. Finally, to evaluate our approach, we compute the Segmental Signal to Noise Ratio (called segmental SNR which is a special SNR very useful for segmented signals.), and Root Mean Square Error (RMSE), both of which are suitable criteria for sound signals, decisive for us in order to show the effectiveness of these different methods.","PeriodicalId":253898,"journal":{"name":"2022 19th International Multi-Conference on Systems, Signals & Devices (SSD)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 19th International Multi-Conference on Systems, Signals & Devices (SSD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSD54932.2022.9955803","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Snoring is a disagreeable sound produced by humans while they sleep and in some dimensions, it is considered pathology. Characterized by inspiratory signals, it is closely related to the breathing function. This paper deals with the sleeping snore using an efficient approach based on the synthesis of a recorded snoring signal. The advantages of this approach are very varied such as offers of a non-contact substitute, artificial reproduction by machine of the original signal (snoring), which can even be integrated later in humanoid robots as an example. The method itself of this reconstitution is a reproduce of the signal through the application of some predictive techniques such as LPC (linear predictive coding), and CELP (Code-excited linear prediction). The difference between original and synthetic signals, called also residuals, can be explained by a scanning factor and different types of noises. Finally, to evaluate our approach, we compute the Segmental Signal to Noise Ratio (called segmental SNR which is a special SNR very useful for segmented signals.), and Root Mean Square Error (RMSE), both of which are suitable criteria for sound signals, decisive for us in order to show the effectiveness of these different methods.