نتایج جستجو برای: short time fourier transform
تعداد نتایج: 2318183 فیلتر نتایج به سال:
In this paper we propose a method to estimate and transform harmonic components in wide-band conditions, out of a single period of the analyzed signal. This method allows estimating harmonic parameters with higher temporal resolution than typical Short Time Fourier Transform (STFT) based methods. We also discuss transformations and synthesis strategies in such context, focusing on the human voice.
In recent years, the synchrosqueezing transform (SST) has gained popularity as a method for the analysis of signals that can be broken down into multiple components determined by instantaneous amplitudes and phases. One such version of SST, based on the short-time Fourier transform (STFT), enables the sharpening of instantaneous frequency (IF) information derived from the STFT, as well as the s...
A projectile's trajectory can be estimated by measuring the arrival times of the acoustic energy at several locations in space. In case of a supersonic projectile fired from a gun, both the acoustic shockwave and muzzle blast can be observed. For acoustic sensor networks attempting to determine the trajectory, the challenge is to first, correctly classify the transient signal as either a shockw...
The frequency contents of the non-stationary signals vary with time. For proper characterization of such signals, a smart time-frequency representation is necessary. Classically, the STFT (short-time Fourier transform) is employed for this purpose. Its limitation is the fixed timefrequency resolution. To overcome this drawback an enhanced STFT version is devised. It is based on the signal drive...
Received Jul 8, 2017 Revised Nov 20, 2017 Accepted Dec 11, 2017 This paper presents a novel audio de-noising scheme in a given speech signal. The recovery of original from the communication channel without any noise is a difficult task. Many de-noising techniques have been proposed for the removal of noises from a digital signal. In this paper, an audio denoising technique based on Short Time F...
A special case of the short-time Fourier transform with single-sided exponential window, named exponentially forgetting transform (EFT), is discussed. It is shown that the EFT offers in a certain sense good time-frequency resolution and that stable reconstruction of a signal from samples of the EFT at equidistant time-frequency grid points (nT, kSZ) is possible, even for the case of nonredundan...
Polynomial phase signals (PPSs) have numerous applications in many fields including radar, sonar, geophysics, and radio communication systems. Therefore, estimation of PPS coefficients is very important. In this paper, a novel approach for PPS parameters estimation based on adaptive short-time Fourier transform (ASTFT), called the PPS-ASTFT estimator, is proposed. Using the PPS-ASTFT estimator,...
Vibration problems in rotors can be extremely frustrating and may lead to greatly reduced reliability. By utilizing the proper data collection and analysis techniques, the faults because of vibration can be discovered and predicted. The signal analysis is important in extracting fault characteristics in fault diagnosis of machinery. The traditional signal analysis can not settle for non-station...
S-transform is a new time-frequency analysis method, which is deduced from short-time Fourier transform and continue Wavelet transform. It has much better performance than traditional time-frequency method. Therefore, in this paper, the basic principle of is briefly introduced and the relationships between is analyzed by theoretical derivation. According to the simulation experiments, the time-...
Time-frequency representation of seismic signals provides a source of information that is usually hidden in the Fourier spectrum. The short-time Fourier transform and the wavelet transform are the principal approaches to simultaneously decompose a signal into time and frequency components. Known limitations, such as trade-offs between time and frequency resolution, may be overcome by alternativ...
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