Short Time Fourier Transform Analysis of Multi-Component Nonstationary Acoustic Signal

2011 ◽  
Vol 403-408 ◽  
pp. 3163-3165 ◽  
Author(s):  
Zhi Bin Gao
Keyword(s):  
Fourier Transform ◽  
Frequency Analysis ◽  
Time Domain ◽  
Frequency Characteristic ◽  
Acoustic Signal ◽  
Short Time Fourier Transform ◽  
Characteristic Parameters ◽  
Time Frequency ◽  
Analysis Technique ◽  
Short Time

In order to extract major components of signal, multi-component nonstationary acoustic signal was analyzed with time-frequency analysis technique. By transforming multi-component nonstationary acoustic signal from time domain to time-frequency domain with short time Fourier transform, major components were determined according to spectrogram. Results show that major components and its time-frequency characteristic parameters can be extracted exactly. Short time Fourier transform is an effective method for extracting major components of nonstationary acoustic signal.

scholarly journals Time-frequency analysis on gong timor music using short-time fourier transform and continuous wavelet transform

2017 ◽  
Vol 3 (3) ◽  
pp. 146
Author(s):  
Yovinia Carmeneja Hoar Siki ◽  
Natalia Magdalena Rafu Mamulak
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Frequency Analysis ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Short Time Fourier Transform ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Music Signal ◽  
Short Time

Time-Frequency Analysis on Gong Timor Music has an important role in the application of signal-processing music such as tone tracking and music transcription or music signal notation. Some of Gong characters is heard by different ways of forcing Gong himself, such as how to play Gong based on the Player’s senses, a set of Gong, and by changing the tempo of Gong instruments. Gong's musical signals have more complex analytical criteria than Western music instrument analysis. This research uses a Gong instrument and two notations; frequency analysis of Gong music frequency compared by the Short-time Fourier Transform (STFT), Overlap Short-time Fourier Transform (OSTFT), and Continuous Wavelet Transform (CWT) method. In the STFT and OSTFT methods, time-frequency analysis Gong music is used with different windows and hop size while CWT method uses Morlet wavelet. The results show that the CWT is better than the STFT methods.

scholarly journals Area-Efficient Short-Time Fourier Transform Processor for Time–Frequency Analysis of Non-Stationary Signals

2020 ◽  
Vol 10 (20) ◽  
pp. 7208
Author(s):  
Hohyub Jeon ◽  
Yongchul Jung ◽  
Seongjoo Lee ◽  
Yunho Jung
Keyword(s):  
Fourier Transform ◽  
Real Time ◽  
Frequency Analysis ◽  
Path Delay ◽  
Window Length ◽  
Short Time Fourier Transform ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Stationary Signals ◽  
Short Time

In this paper, we propose an area-efficient short-time Fourier transform (STFT) processor that can perform time–frequency analysis of non-stationary signals in real time, which is essential for voice or radar-signal processing systems. STFT processors consist of a windowing module and a fast Fourier transform processor. The length of the window function is related to the time–frequency resolution, and the required window length varies depending on the application. In addition, the window function needs to overlap the input data samples to minimize the data loss in the window boundary, and overlap ratios of 25%, 50%, and 75% are generally used. Therefore, the STFT processor should ideally support a variable window length and overlap ratio and be implemented with an efficient hardware architecture for real-time time–frequency analysis. The proposed STFT processor is based on the radix-4 multi-path delay commutator (R4MDC) pipeline architecture and supports a variable length of 16, 64, 256, and 1024 and overlap ratios of 25%, 50%, and 75%. Moreover, the proposed STFT processor can be implemented with very low complexity by having a relatively lower number of delay elements, which are the ones that increase complexity in the most STFT processors. The proposed STFT processor was designed using hardware description language (HDL) and synthesized to gate-level circuits using a standard cell library in a 65 nm CMOS process. The proposed STFT processor results in logic gates of 197,970, which is 63% less than that of the conventional radix-2 single-path delay feedback (R2SDF) based STFT processor.

An Adaptive Time–Frequency Representation and its Fast Implementation

2006 ◽  
Vol 129 (2) ◽  
pp. 169-178 ◽  
Author(s):  
Bao Liu ◽  
Sherman Riemenschneider ◽  
Zuowei Shen
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Frequency Analysis ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Frequency Representation ◽  
Adaptive Time ◽  
Short Time

This paper presents a fast adaptive time–frequency analysis method for dealing with the signals consisting of stationary components and transients, which are encountered very often in practice. It is developed based on the short-time Fourier transform but the window bandwidth varies along frequency adaptively. The method therefore behaves more like an adaptive continuous wavelet transform. We use B-splines as the window functions, which have near optimal time–frequency localization, and derive a fast algorithm for adaptive time–frequency representation. The method is applied to the analysis of vibration signals collected from rotating machines with incipient localized defects. The results show that it performs obviously better than the short-time Fourier transform, continuous wavelet transform, and several other most studied time–frequency analysis techniques for the given task.

Research on Joint Time-Frequency Analysis Methods of Adaptive Radar Signal

2014 ◽  
Vol 989-994 ◽  
pp. 4009-4013 ◽  
Author(s):  
Qiang Xing ◽  
Wei Gang Zhu ◽  
Yuan Bo ◽  
Kang Wang
Keyword(s):  
Fourier Transform ◽  
Frequency Analysis ◽  
Signal Analysis ◽  
Radar Signal ◽  
Analysis Method ◽  
Short Time Fourier Transform ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Adaptive Radar ◽  
Short Time

Faced with complex electromagnetic environment and varieties of adaptive radar waveforms, radar signal analysis and identification becomes more and more complex. Considering two important physical quantities - time and frequency in modern signal processing methods, this paper proposes that the joint time-frequency analysis (JTFA) method based on fractional Fourier transform (FrFT) and short-time Fourier transform (STFT) is applied to adaptive radar signal processing. The simulation results show that the joint time-frequency analysis method is superior to single short-time Fourier transform, getting a better analysis of results. The joint time-frequency analysis method provides the joint distribution of the time domain and frequency domain for adaptive radar signal analysis and describes the relationship between signal frequency and time.

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