Spectrogram: Difference between revisions

Revision as of 18:20, 18 December 2023

Revision as of 17:53, 26 December 2023

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* 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’sChina's enormous surveillance state is still growing |worknewspaper=[[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 spectrogram’s color scale identifies the frequencies of a waveform’s amplitude peaks over time. Unlike a time or frequency graph, a spectrogram correlates peak values to time and frequency. Vibration test engineers use spectrograms to analyze the frequency content of a continuous waveform, locating strong signals and determining how the vibration behavior changes over time. <ref>{{Cite web|url=https://vibrationresearch.com/blog/what-is-a-spectrogram/|title=What is a Spectrogram? | access-date=2023-12-18}}</ref>

* 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|url=https://link.springer.com/article/10.1007/s10044-020-00921-5|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=24 September 20202021|journal=Pattern Analysis and Applications|volume=24 |issue=2 |pages=423–431 |doi=10.1007/s10044-020-00921-5 }}</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 speakers' pronunciation from a linear superposition of short-time spectrograms.<ref>{{cite journal|url=https://link.springer.com/article/10.1007/s40747-020-00172-1|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=29 June 20202021|journal=Complex & Intelligent Systems|volume=7 |issue=4 |pages=1749–1757 |doi=10.1007/s40747-020-00172-1 }}</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=14 March 20212022|journal=Journal of Ambient Intelligence and Humanized Computing|volume=13 |issue=2 |pages=757–767 |doi=10.1007/s12652-021-02926-2 }}</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?casa_token=xQTOY4_RhuUAAAAA:S6rUQ7P6o9bN8-apZvE6c0GL4vOC6t0E7SCfmGYdVu8NRF3L4-YItNTvrlUlDycRpWF3qdVTeQ|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}}</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|url=https://www.mdpi.com/2076-3417/12/4/1895|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}}</ref>