Advanced Time-Frequency Representation in Voice Signal Analysis
نویسندگان
چکیده
منابع مشابه
Advanced Concepts in Time-frequency Signal Processing Made Simple
The authors are with the Department of Electrical Engineering, Arizona State University, Tempe, AZ 85287. (Emails: [email protected], [email protected], [email protected]) This work was supported by the National Science Foundation grant CCLI DUE – 0089075. Abstract Time -frequency representations (TFRs) such as the spectrogram are important two-dimensional tools for processing time-varying sign...
متن کاملTime-frequency Techniques in Biomedical Signal Analysis
Objectives: This review outlines the method ological fundamentals of the most frequently used non-parametric time-frequency analysis techniques in biomedicine and their main properties, as well as providing decision aids concerning their applications. Methods: The short-term Fourier transform (STFT), the Gabor transform (GT), the S-transform (ST), the continuous Morlet wavelet transform (CMWT),...
متن کاملTime-frequency Representation for Classification of the Transient Myoelectric Signal
An accurate and computationally efficient means of classifying myoelectric signal (MES) patterns has been the subject of considerable research effort in recent years. Effective feature extraction is crucial to reliable classification and, in the quest to improve the accuracy of transient MES pattern classification, many forms of signal representation have been suggested. It is shown that featur...
متن کاملA signal-dependent time-frequency representation: optimal kernel design
Time-frequency distributions (TFD’s), which indicate the energy content of a signal as a function of both time and frequency, are powerful tools for time-varying signal analysis. The lack of a single distribution that is “best” for all applications has resulted in a proliferation of TFD’s, each corresponding to a different, fixed mapping from signals to the time-frequency plane. A major drawbac...
متن کاملTime –Frequency Representation of Vocal Source Signal for Speaker Verification
We propose an effective feature extraction technique for obtaining essential time-frequency information from the linear prediction (LP) residual signal, which are closely related to the glottal vibration of individual speaker. With pitch synchronous analysis, wavelet transform is applied to every two pitch cycles of the LP residual signal to generate a new feature vector, called Wavelet Based F...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Advances in Science and Technology Research Journal
سال: 2018
ISSN: 2080-4075,2299-8624
DOI: 10.12913/22998624/87028