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Spectrogram

About: Spectrogram is a research topic. Over the lifetime, 5813 publications have been published within this topic receiving 81547 citations.


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Proceedings ArticleDOI
01 Dec 2008
TL;DR: In this paper, the authors presented the implementation of linear time-frequency distribution (TFD) techniques to analyze power quality signals, including the average of root mean square (RMS) voltage, total waveform distortion, total harmonic distortion and total nonharmonic distortion.
Abstract: This paper presents the implementation of linear time-frequency distribution (TFD) techniques to analyze power quality signals The power quality signals are swell, sag, interruption, transient, harmonic, interharmonic, notching and normal voltage based on IEEE Std 1159-1995 The time-frequency analysis techniques selected are spectrogram and Gabor transform From the time-frequency representation (TFR) obtained, the signal parameters are estimated to identify the signal characteristics Identified signal characteristics that are used as input to a classifier includes the average of root mean square (RMS) voltage, total waveform distortion, total harmonic distortion and total nonharmonic distortion and duration of swell, sag, interruption and transient From the linear time-frequency analysis techniques used, the optimal technique is chosen in terms of accuracy, computation complexity and memory size

26 citations

Journal ArticleDOI
TL;DR: This article shows how sparse codes can be used to do continuous speech recognition by using an iterative subset selection algorithm with quadratic programming to find a sparse code for a spectrogram.

26 citations

01 Jan 1984
TL;DR: It is demonstrated that computation of the WDF of real 2-D signals is susceptible to Radon transform solution, and the optical setup is easily modified to produce the cross-Wigner distribution function, a special case of the complex, or windowed, spectrogram.
Abstract: The Wigner distribution function (WDF), a simultaneous coordinate and frequency representation of a signal, has properties useful in pattern recognition. Because the WDF is computationally demanding, its use is not usually appropriate in digital processing. Optical schemes have been developed to compute the WDF for one-dimensional (1 -D) signals, often using acousto-optic signal transducers. Some recent work has demonstrated the computation of two-dimensional (2-D) slices of the four-dimensional (4-D) WDF of a 2-D input transparency. In this latter case, the required 2-D Fourier transformation is performed by coherent optics. We demonstrate that computation of the WDF of real 2-D signals is susceptible to Radon transform solution. The 2-D operation is reduced to a series of 1 -D operations on the line-integral projections. The required projection data are produced optically, and the Fourier transformation is performed by efficient 1 -D processors (surface acoustic wave filters) by means of the chirp-transform algorithm. The resultant output gives 1 -D slices through the 4-D WDF nearly in real time, and the computation is not restricted to coherently illuminated transparencies. This approach may be useful in distinguishing patterns with known texture direction. The optical setup is easily modified to produce the cross-Wigner distribution function, a special case of the complex, or windowed, spectrogram.

26 citations

Journal ArticleDOI
TL;DR: An automated infant cry analyzer with high accuracy to detect important acoustic features of cry is described and validated, which has implications for basic and applied research on infant cry development.
Abstract: Purpose In this article, the authors describe and validate the performance of a modern acoustic analyzer specifically designed for infant cry analysis. Method Utilizing known algorithms, the authors developed a method to extract acoustic parameters describing infant cries from standard digital audio files. They used a frame rate of 25 ms with a frame advance of 12.5 ms. Cepstral-based acoustic analysis proceeded in 2 phases, computing frame-level data and then organizing and summarizing this information within cry utterances. Using signal detection methods, the authors evaluated the accuracy of the automated system to determine voicing and to detect fundamental frequency (F0) as compared to voiced segments and pitch periods manually coded from spectrogram displays. Results The system detected F0 with 88% to 95% accuracy, depending on tolerances set at 10 to 20 Hz. Receiver operating characteristic analyses demonstrated very high accuracy at detecting voicing characteristics in the cry samples. Conclusions...

26 citations

Proceedings ArticleDOI
01 Sep 2019
TL;DR: It is demonstrated that features with specific physical meaning like spectrogram are less effective than a combination of such features and neural networks.
Abstract: Forearm surface electromyography (sEMG) classification for hand movements is a trending research field in several real-life scenarios. The classification, also known as the decoding, is helpful to building dexterous prostheses and dexterous exoskeleton for soldiers. According to previous studies, the classification accuracy is subject to the features extracted from the source signals. The traditional features are usually very carefully designed physical signals. In this paper, we demonstrate that features with specific physical meaning like spectrogram are less effective than a combination of such features and neural networks. Tests are performed on Ninapro database which contains 40 subjects' sEMG data sampled by 12-channel surface electrodes for 50 different movements including finger/wrist gestures and force exertion. Our methods combines the spectrogram, the CNN and the LSTM to fully use the spacial local physical information and sequence's time information. The results show improved classification accuracy (from 75.740% to 80.929% for the basic hand gestures and an overall improvement from 77.167% to 79.329%).

26 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
2023627
20221,396
2021488
2020595
2019593