{"title":"基于fft的听觉频谱及其在音频分类中的应用","authors":"Wei Chu, B. Champagne","doi":"10.1109/ICOSP.2008.4697712","DOIUrl":null,"url":null,"abstract":"In this paper, the noise-robustness of a recently proposed fast Fourier transform (FFT)-based auditory spectrum (FFT-AS) is further evaluated through speech/music/noise classification experiments wherein mismatched test cases are considered. The features obtained from the FFT-AS show more robust performance as compared to the conventional mel-frequency cepstral coefficient (MFCC) features. To further explore the FFT-AS from a perspective of practical audio classification, an audio classification algorithm using features derived from the FFT-AS is implemented on the floating-point DSP platform TMS320C6713. Through various optimization approaches, a significant reduction in the computational complexity is achieved wherein the implemented system demonstrates the ability to classify among speech, music and noise under the constraint of real-time processing.","PeriodicalId":445699,"journal":{"name":"2008 9th International Conference on Signal Processing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Further studies of a FFT-based auditory spectrum with application in audio classification\",\"authors\":\"Wei Chu, B. Champagne\",\"doi\":\"10.1109/ICOSP.2008.4697712\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, the noise-robustness of a recently proposed fast Fourier transform (FFT)-based auditory spectrum (FFT-AS) is further evaluated through speech/music/noise classification experiments wherein mismatched test cases are considered. The features obtained from the FFT-AS show more robust performance as compared to the conventional mel-frequency cepstral coefficient (MFCC) features. To further explore the FFT-AS from a perspective of practical audio classification, an audio classification algorithm using features derived from the FFT-AS is implemented on the floating-point DSP platform TMS320C6713. Through various optimization approaches, a significant reduction in the computational complexity is achieved wherein the implemented system demonstrates the ability to classify among speech, music and noise under the constraint of real-time processing.\",\"PeriodicalId\":445699,\"journal\":{\"name\":\"2008 9th International Conference on Signal Processing\",\"volume\":\"8 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.2008.4697712\",\"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.2008.4697712","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Further studies of a FFT-based auditory spectrum with application in audio classification
In this paper, the noise-robustness of a recently proposed fast Fourier transform (FFT)-based auditory spectrum (FFT-AS) is further evaluated through speech/music/noise classification experiments wherein mismatched test cases are considered. The features obtained from the FFT-AS show more robust performance as compared to the conventional mel-frequency cepstral coefficient (MFCC) features. To further explore the FFT-AS from a perspective of practical audio classification, an audio classification algorithm using features derived from the FFT-AS is implemented on the floating-point DSP platform TMS320C6713. Through various optimization approaches, a significant reduction in the computational complexity is achieved wherein the implemented system demonstrates the ability to classify among speech, music and noise under the constraint of real-time processing.