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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.


Papers
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Journal ArticleDOI
TL;DR: The possibility of remote voice recognition using Doppler information, with or without a barrier, is demonstrated, and the VGG-16 model, its accuracy was improved up to 97%.
Abstract: We investigated the feasibility of using Doppler radar to recognize human voices by capturing the micro-Doppler signatures of vibrations from the larynx and mouth. The signatures produced through the vibrations of a human being’s vocal cords generate unique micro-Doppler signatures, depending on the letters pronounced. These can then be used to classify and recognize different words and letters. In this paper, we could successfully capture echo signals using the Doppler radar when a human subject spoke seven musical notes from Do to Ti and alphabet letters from $\boldsymbol {A}$ to $\boldsymbol {Z}$ . Spectrogram analysis was conducted for classification purposes, and the deep convolutional neural networks employed could classify the 26 letters to an accuracy of 94%. To overcome the deficiency of the measured data and improve the classification accuracy, transfer learning was introduced. Using the VGG-16 model, its accuracy was improved up to 97%. Additional experiments were conducted to ascertain the radar’s capability to detect the human voice through a barrier between the human and the radar. In this paper, we demonstrated the possibility of remote voice recognition using Doppler information, with or without a barrier.

29 citations

Journal ArticleDOI
TL;DR: In this article, the authors used geometrical arguments and analysis of an idealised mathematical model to identify features of spectrograms, concentrating on the effects of a finite-depth channel.

29 citations

Patent
05 Aug 2015
TL;DR: In this paper, a rolling bearing sound signal fault diagnosis method based on short-time Fourier transform and a sparse laminated automatic encoder was proposed, which can accurately determine the fault mode of the rolling bearing.
Abstract: The invention discloses a rolling bearing sound signal fault diagnosis method based on short-time Fourier transform and a sparse laminated automatic encoder. According to the method of the invention, firstly a smart mobile phone is used for acquiring a sound signal of the rolling bearing fault; then short-time Fourier analysis is performed on the sound signal for obtaining a spectrogram matrix; then the modulus value of the matrix is acquired and gray scale normalization processing is performed; then the normalized data are selected and input into a deep studying network for automatically extracting characteristics; and finally the characteristic which is extracted by a neural net is input into a Softmax classifier for identifying the fault mode. The invention provides the rolling bearing sound signal fault diagnosis method based on smart mobile phone sound signal short-time Fourier transform (STFT) and the sparse laminated automatic encoder (SAE). Through testing result analysis, the rolling bearing sound signal fault diagnosis method can accurately determine the fault mode of the rolling bearing.

29 citations

Journal ArticleDOI
TL;DR: The best backgrounds in terms of generalisation capabilities are found to be backgrounds in which some component of speech is present, which corroborates the hypothesis that the AMS features provide a decomposition of signals which is by itself very suitable for training very general speech/nonspeech detectors.

29 citations

Journal ArticleDOI
TL;DR: The usage of coprime sampling for calculating ambiguity function of matched filter in radar system is investigated and the effect of it is examined, and several useful guidelines of choosing configuration to conduct the sparse sensing while retain the detection quality are concluded.
Abstract: Estimating the spectrogram of non-stationary signal relates to many important applications in radar signal processing. In recent years, coprime sampling and array attract attention for their potential of sparse sensing with derivative to estimate autocorrelation coefficients with all lags, which could in turn calculate the power spectrum density. But this theoretical merit is based on the premise that the input signals are wide-sense stationary. In this article, we discuss how to implement coprime sampling for non-stationary signal, especially how to attain the benefits of coprime sampling meanwhile limiting the disadvantages due to lack of observations for estimations. Furthermore, we investigate the usage of coprime sampling for calculating ambiguity function of matched filter in radar system. We also examine the effect of it and conclude several useful guidelines of choosing configuration to conduct the sparse sensing while retain the detection quality.

29 citations


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