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* The studies of [[phonetics]] and [[speech synthesis]] are often facilitated through the use of spectrograms.<ref>{{cite web|url=http://cslu.cse.ogi.edu/tutordemos/SpectrogramReading/spectrogram_reading.html|title=Spectrogram Reading|website=ogi.edu|access-date=7 April 2018|url-status=dead|archive-url=https://web.archive.org/web/19990427185722/http://cslu.cse.ogi.edu/tutordemos/SpectrogramReading/spectrogram_reading.html |archive-date=27 April 1999}}</ref><ref>{{cite web|url=http://www.fon.hum.uva.nl/praat/|title=Praat: doing Phonetics by Computer|website=www.fon.hum.uva.nl|access-date=7 April 2018}}</ref>
* In deep learning-keyed speech synthesis, spectrogram (or spectrogram in [[mel scale]]) is first predicted by a seq2seq model, then the spectrogram is fed to a neural vocoder to derive the synthesized raw waveform.
* By reversing the process of producing a spectrogram, it is possible to create a signal whose spectrogram is an arbitrary image. This technique can be used to hide a picture in a piece of audio and has been employed by several [[electronic music]] artists.<ref>{{cite web|url=http://www.bastwood.com/aphex.php|title=The Aphex Face – bastwood|website=www.bastwood.com|access-date=7 April 2018}}</ref> See also [[
* Some modern music is created using spectrograms as an intermediate medium; changing the intensity of different frequencies over time, or even creating new ones, by drawing them and then inverse transforming. See [[Audio timescale-pitch modification]] and [[Phase vocoder]].
* Spectrograms can be used to analyze the results of passing a test signal through a signal processor such as a filter in order to check its performance.<ref>{{cite web|url=http://src.infinitewave.ca|title=SRC Comparisons|website=src.infinitewave.ca|access-date=7 April 2018}}</ref>
* High definition spectrograms are used in the development of RF and microwave systems.<ref>{{cite web|url=http://www.constantwave.com/gallery.aspx|title=constantwave.com – constantwave Resources and Information.|website=www.constantwave.com|access-date=7 April 2018}}</ref>
* Spectrograms are now used to display [[scattering parameters]] measured with vector network analyzers.<ref>{{cite web |url=http://www.constantwave.com/spectro_vna.aspx |title=Spectrograms for vector network analyzers |archive-url=https://web.archive.org/web/20120810020043/http://www.constantwave.com/spectro_vna.aspx |archive-date=2012-08-10 |url-status=dead }}</ref>
* The [[United States Geological Survey|US Geological Survey]] and the [[IRIS Consortium]] provide near real-time spectrogram displays for monitoring seismic stations<ref>{{cite web|url=https://earthquake.usgs.gov/monitoring/spectrograms/24hr/|title=Real-time Spectrogram Displays|website=earthquake.usgs.gov|access-date=7 April 2018}}</ref><ref>{{Cite web|url=https://service.iris.edu/mustang/noise-spectrogram/docs/1/help/|title=IRIS: MUSTANG: Noise-Spectrogram: Docs: v. 1: Help}}</ref>
* Spectrograms can be used with [[recurrent neural network]]s for [[speech recognition]].<ref>{{Cite web|url=https://medium.com/@ageitgey/machine-learning-is-fun-part-6-how-to-do-speech-recognition-with-deep-learning-28293c162f7a|title=Machine Learning is Fun Part 6: How to do Speech Recognition with Deep Learning|last=Geitgey|first=Adam|date=2016-12-24|website=Medium|access-date=2018-03-21}}</ref><ref>See also [[Praat]].</ref>
* Individuals' spectrograms are collected by the [[Government of China|Chinese government]] as part of its [[Mass surveillance in China|mass surveillance]] programs.<ref>{{Cite news |date=November 23, 2023 |title=China's enormous surveillance state is still growing |newspaper=[[The Economist]] |url=https://www.economist.com/china/2023/11/23/chinas-enormous-surveillance-state-is-still-growing |url-access=subscription |access-date=2023-11-25 |issn=0013-0613}}</ref>
* For a vibration signal, a
* Spectrograms can be used to analyze speech in two different applications: automatic detection of speech deficits in cochlear implant users and phoneme class recognition to extract phone-attribute features.
* In order to obtain a speaker's pronunciation characteristics, some researchers proposed a method based on an idea from bionics, which uses spectrogram statistics to achieve a characteristic spectrogram to give a stable representation of the
* Researchers explore a novel approach to ECG signal analysis by leveraging spectrogram techniques, possibly for enhanced visualization and understanding. The integration of MFCC for feature extraction suggests a cross-disciplinary application, borrowing methods from audio processing to extract relevant information from biomedical signals.<ref>{{cite journal|url=https://link.springer.com/article/10.1007/s12652-021-02926-2|title=Spectrogram analysis of ECG signal and classification efficiency using MFCC feature extraction technique|first1=Arpitha |last1= Yalamanchili |first2= G. L.|last2=Madhumathi |first3= N.|last3=Balaji |date=2022|journal=Journal of Ambient Intelligence and Humanized Computing|volume=13 |issue=2 |pages=757–767 |doi=10.1007/s12652-021-02926-2 |s2cid=233657057 }}</ref>
* Accurate interpretation of temperature indicating paint (TIP) is of great importance in aviation and other industrial applications. 2D spectrogram of TIP can be used in temperature interpretation.
* The spectrogram can be used to process the signal for the rate of change of the human thorax. By visualizing respiratory signals using a spectrogram, the researchers have proposed an approach to the classification of respiration states based on a neural network model.
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