The Sound Spectrograph

Koenig, Walter D.; Dunn, H. K.; Lacy, L. Y.

doi:10.1121/1.1902419

Cited by 122 publications

(76 citation statements)

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“…The description of spectral analysis by Koenig, Dunn, and Lacy (1946) gives a good example of frequency modulation, in which a sine-wave tone is varied (in frequency) sinusoidally, and the authors comment that "when warble rate is comparable to the filter width the warble breaks up into discrete side-band frequencies". Analysis of a varying rate of broadband pulses is presented also, showing clearly the repetition-rate information.…”

Section: Figmentioning

confidence: 99%

“…This is especially important with the advent of analyzers permitting expanded scales with different portions of the spectrum opened and drawn full-width during analysis. The statement of the effective filter bandwidth (as was given by Koenig, Dunn and Lacy, 1946) reduces ambiguities resulting from speed changes and helps keep in focus some of the various artifacts of the machine. The filter bandwidth just as surely labels the parameters of the discrimination offrequency and time as the scales define which frequencies and times are portrayed.…”

Section: Figmentioning

confidence: 99%

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The harmonic interval : fact or artifact in spectral analysis of pulse trains

Watkins¹

1968

109

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Section: Figmentioning

confidence: 99%

Section: Figmentioning

confidence: 99%

The harmonic interval : fact or artifact in spectral analysis of pulse trains

Watkins¹

1968

109

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“…The problem of detecting tracks in a spectrogram (also known as a LOFARgram, periodogram, sonogram, or spectral waterfall), particularly in underwater environments, has been investigated since the spectrogram's introduction in the mid 1940s by Koenig et al [26]. Research into the use of automatic detection methods increased with the advent of reliable computational algorithms during the 1980s, 1990s and early 21st century.…”

Section: Introductionmentioning

confidence: 99%

A detailed investigation into low-level feature detection in spectrogram images

Lampert

O’Keefe

2011

Pattern Recognition

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Being the first stage of analysis within an image, low-level feature detection is a crucial step in the image analysis process and, as such, deserves suitable attention. This paper presents a systematic investigation into low-level feature detection in spectrogram images. The result of which is the identification of frequency tracks. Analysis of the literature identifies different strategies for accomplishing low-level feature detection. Nevertheless, the advantages and disadvantages of each are not explicitly investigated. Three model-based detection strategies are outlined, each extracting an increasing amount of information from the spectrogram, and, through ROC analysis, it is shown that at increasing levels of extraction the detection rates increase. Nevertheless, further investigation suggests that model-based detection has a limitation-it is not computationally feasible to fully evaluate the model of even a simple sinusoidal track. Therefore, alternative approaches, such as dimensionality reduction, are investigated to reduce the complex search space. It is shown that, if carefully selected, these techniques can approach the detection rates of model-based strategies that perform the same level of information extraction. The implementations used to derive the results presented within this paper are available online from http://stdetect.googlecode.com.

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“…Sound spectrogram was first introduced during World War II for military purposes. The Bell laboratories in 1941 successfully developed a voice visualization instrument called sound spectrograph for mapping a voice onto a graph (Koenig et al, 1946). The instrument was initially used to identify enemy voices on telephones and radios, and has gradually become an indispensable tool in speech analysis and phonetics studies.…”

Section: Introductionmentioning

confidence: 99%

A Visualization Scheme for Complex Spectrograms with Complete Amplitude and Phase Information

Zheng¹,

Li²,

Xu³

et al. 2017

dtmse

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We present a novel visualization scheme for complex spectrograms of speech and other non-stationary signals, in which both the amplitude and phase information is visualized simultaneously using the conventional RGB (red, green, blue) color model. The three-layer image matrix is constructed in such a way that the absolute values of the real and imaginary parts of the time-frequency analysis of speech are used to fill the first (red) and third (blue) layers, respectively, and an encoding scheme is adopted to represent both signs of the real and imaginary parts and the computed values based on this encoding scheme are used to fill the second (green) layer. The importance of phase in spectrogram applications has been gradually recognized, and imaging processing techniques are increasingly used in spectral analysis of non-stationary one-dimensional (1D) signals such as speech signal. The ability to present complete spectrogram information (both the amplitude and phase information) on a single RGB image is potentially useful, especially for those applications in which phase information plays a complementary role.

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The Sound Spectrograph

Abstract: FK;. 1. Illustrating one method which has been used in the past to show how vocal resonances change with time. This is a series of harmonic analyses of successive periods of the vowel in the word "out." 19

Cited by 122 publications

References 0 publications

The harmonic interval : fact or artifact in spectral analysis of pulse trains

The harmonic interval : fact or artifact in spectral analysis of pulse trains

A detailed investigation into low-level feature detection in spectrogram images

A Visualization Scheme for Complex Spectrograms with Complete Amplitude and Phase Information

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