Abstract-This paper presents the various methods for the spectral analysis of signals for the stationary as well as non-stationary signals. Due to non-stationary characteristics of the signals, it has been always a challenge to achieve time frequency distribution of such signals. Between the various techniques of signal analysis, this paper uses Fourier transform, Short time Fourier transform, ...

-Motor Current Signature Analysis (MCSA) has been successfully used for fault diagnosis in induction machines. The current spectrum of the induction machine for locating characteristic fault frequencies is used in MCSA. The spectrum is obtained using a Fast Fourier Transformation (FFT) that is performed on the signal under analysis. The fault frequencies occur in the motor current spectra are u...

In this paper, we present a content-based classification approach for audio indexing. The classification is based on low level audio features such as temporal sound, energy, fundamental frequency, zero crossing rate, auto correlation curve, and based on transformations such as Short Time Fourier Transform (STFT). The audio classification in classes such as music, noise, silence, and speech is a...

This paper presents a technique to provide an acoustic representation of electroencephalogram (EEG) data using neural networks. The sample EEG consists of actual random movements of left and right hand recorded with eyes closed of a 21-year old, right handed male with no known medical conditions. In addition, an EEG signal simulator was used to generate random EEG signals aside from the actual ...

The Short Term Fourier Transform (STFT) is a classical linear time-frequency (T-F) representation. Despites its relative simplicity, it has become a standard tool for the analysis of non-stationary signals. Since it provides a redundant representation, it raises some issues such as (i) “optimal” window choice for analysis, (ii) existence and determination of an inverse transformation, (iii) per...

Recently, a direct method of the time–frequency approach, called signal separation operator (SSO), which is based on sinusoidal approximation, was introduced to solving inverse problem multicomponent separation. In very recent paper “Direct via extraction local frequencies with adaptive time-varying parameters”, authors obtained more accurate component recovery formula derived from linear chirp...

In 1995-1996 the Acoustic Thermometry of Ocean Climate (ATOC) experiment provided an opportunity to study long-range broadband transmissions over a series of months using mode-resolving vertical arrays. A 75-Hz source off the California coast transmitted broadband pulses to receiving arrays in the North Pacific, located at ranges of 3515 and 5171 km. This paper develops a short-time Fourier tra...

We show that a stationary signal concept such as the Fourier transform (FT) can be linked to a time-varying (TV) concept such as linear TV filtering. In particular, we give a continuous-time and discrete-time linear TV filter interpretation to continuous-time FT and discrete Fourier transform (DFT), respectively. As a consequence, important variants such as warped DFT, DCT, DST also can be view...

The short-time Fourier transform (STFT) and the continuous wavelet transform (CWT) are used to analyze the time course of cellular motion in the inner ear. The velocity responses of individual outer hair cells and Hensen’s cells to sinusoidal and amplitude modulated (AM) acoustical signals applied at the ear canal display characteristics typical of nonlinear systems, including the generation of...

Feature extraction is one of the most important tasks in speech recognition system. Most of the speech recognition systems use Short Time Fourier Transform (STFT) for the derivation of features from the spoken utterances. In this paper we try to exploit the higher time–frequency resolution property of Discrete Wavelet Transform (DWT) for extraction of speaker independent features. The features ...