Spectrogram: Difference between revisions

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{{redirect|Sonograph|the musical recording|Sonograph (EP)}}

{{for|the scientific instrument|Optical spectrograph}}

[[Image:Spectrogram-19thC.png|thumb|400pxupright=1.35|Spectrogram of the spoken words "nineteenth century". Frequencies are shown increasing up the vertical axis, and time on the horizontal axis. The legend to the right shows that the color intensity increases with the density.]]

[[File:3D battery charger RF spectrum over time.jpg|thumb|400pxupright=1.35|A 3D spectrogram: The RF spectrum of a battery charger is shown over time]]

A '''spectrogram''' is a visual representation of the [[spectral density|spectrum]] of [[frequencies]] of a signal as it varies with time.

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* 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>

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* 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.<ref>{{cite journal|title=Multi-channel spectrograms for speech processing applications using deep learning methods|first1=Arias-Vergara |last1= T. |first2= Klumpp|last2=P.|first3= Vasquez-Correa|last3=J. C.|first4=Nöth|last4=E. |first5= Orozco-Arroyave|last5=J. R. |first6=Schuster |last6=M. |date=2021|journal=Pattern Analysis and Applications|volume=24 |issue=2 |pages=423–431 |doi=10.1007/s10044-020-00921-5 |doi-access=free}}</ref>

* 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 speaker's pronunciation from a linear superposition of short-time spectrograms.<ref>{{cite journal|title=Speaker recognition based on characteristic spectrograms and an improved self-organizing feature map neural network|first1=Yanjie |last1= Jia |first2= Xi|last2=Chen|first3= Jieqiong|last3=Yu|first4=Lianming|last4=Wang|first5= Yuanzhe|last5= Xu |first6=Shaojin |last6=Liu |first7=Yonghui |last7=Wang |date=2021|journal=Complex & Intelligent Systems|volume=7 |issue=4 |pages=1749–1757 |doi=10.1007/s40747-020-00172-1 |doi-access=free}}</ref>

* 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 |url-access=subscription }}</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.<ref>{{cite journal|url=https://www.sciencedirect.com/science/article/pii/S0263224123008813|title=Temperature interpretation method for temperature indicating paint based on spectrogram|first1=Junfeng |last1= Ge |first2= Li|last2=Wang |first3= Kang|last3=Gui |first4= Lin|last4=Ye |date=30 September 2023|journal=Measurement|volume=219 |doi=10.1016/j.measurement.2023.113317 |bibcode=2023Meas..21913317G |s2cid=259871198 |url-access=subscription }}</ref>

* 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.<ref>{{cite journal|title=Classification of Respiratory States Using Spectrogram with Convolutional Neural Network|first1=Cheolhyeong |last1= Park |first2= Deokwoo|last2=Lee |date=11 February 2022|journal=Applied Sciences|volume=12 |issue=4 |page=1895 |doi=10.3390/app12041895 |doi-access=free }}</ref>

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==External links==

{{Commons category|SpectrogramSpectrograms}}

{{Wiktionary}}

*[https://auditoryneuroscience.com/acoustics/spectrogram See an online spectrogram of speech or other sounds captured by your computer's microphone.]