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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: It is found that the wavelet transform is capable of detecting the two components, the aortic valve component A2 and pulmonary valve component P2, of the second sound S2 of a normal PCG signal.
Abstract: This paper presents the applications of the spectrogram, Wigner distribution and wavelet transform analysis methods to the phonocardiogram (PCG) signals. A comparison between these three methods has shown the resolution differences between them. It is found that the spectrogram short-time Fourier transform (STFT), cannot detect the four components of the first sound of the PCG signal. Also, the two components of the second sound are inaccurately detected. The Wigner distribution can provide time-frequency characteristics of the PCG signal, but with insufficient diagnostic information: the four components of the first sound, SI, are not accurately detected and the two components of the second sound, S2, seem to be one component. It is found that the wavelet transform is capable of detecting the two components, the aortic valve component A2 and pulmonary value component P2, of the second sound S2 of a normal PCG signal. These components are not detectable using the spectrogram or the Wigner distribution. Ho...

134 citations

Patent
18 Sep 2002
TL;DR: A spectrum analysis engine (SAGE) as mentioned in this paper consists of a spectrum analyzer, a signal detector, a universal signal synchronizer, and a snapshot buffer component, where the signal detector detects signal pulses in the frequency band and outputs pulse event information entries.
Abstract: A spectrum analysis engine (SAGE) that comprises a spectrum analyzer component, a signal detector component, a universal signal synchronizer component and a snapshot buffer component. The spectrum analyzer component generates data representing a real-time spectrogram of a bandwidth of radio frequency (RF) spectrum. The signal detector detects signal pulses in the frequency band and outputs pulse event information entries output, which include the start time, duration, power, center frequency and bandwidth of each detected pulse. The signal detector also provides pulse trigger outputs which may be used to enable/disable the collection of information by the spectrum analyzer and the snapshot buffer components. The snapshot buffer collects a set of raw digital signal samples useful for signal classification and other purposes. The universal signal synchronizer synchronizes to periodic signal sources, useful for instituting schemes to avoid interference with those signals.

134 citations

Journal ArticleDOI
TL;DR: This paper proposes a combination of hand-crafted and deep-learned features which can effectively measure the severity of depression from speech and proposes joint fine-tuning layers to combine the raw and spectrogram DCNN to boost the depression recognition performance.

133 citations

Proceedings ArticleDOI
23 May 1989
TL;DR: The colored-noise prefilter greatly enhances the quality and intelligibility of LPC output speech for noisy inputs, and it is demonstrated that such gains are unavailable with white noise assumption Kalman and Wiener filters.
Abstract: A report is presented on experiments using a colored-noise assumption Kalman filter to enhance speech additively contaminated by colored noise, such as helicopter noise and jeep noise, with a particular application to linear predictive coding (LPC) of noisy speech. The results indicate that the colored-noise Kalman filter provides a significant gain in SNR, a clear improvement in the sound spectrogram, and an audible improvement in output speech quality. The authors demonstrate that such gains are unavailable with white noise assumption Kalman and Wiener filters. The colored-noise prefilter greatly enhances the quality and intelligibility of LPC output speech for noisy inputs. >

132 citations

Proceedings ArticleDOI
03 Apr 1990
TL;DR: Perceptual experiments with trained human listeners revealed that MLPs perform much better than humans on vowels excised from context, and the cochleagram is superior to the spectrogram in classification performance for all experimental conditions.
Abstract: The ability of multilayer perceptrons (MLPs) trained with backpropagation to classify vowels excised from natural continuous speech is examined. Two spectral representations are compared: spectrograms and cochleagrams. The features used to train the MLPs include discrete Fourier transform (DFT) or cochleagram coefficients from a single frame in the middle of the vowel, or coefficients from each third of the vowel. The effects of estimates of pitch, duration, and the relative amplitude of the vowel were investigated. The experiments show that with coefficients alone, the cochleagram is superior to the spectrogram in classification performance for all experimental conditions. With the three additional features, however, the results are comparable. Perceptual experiments with trained human listeners on the same data revealed that MLPs perform much better than humans on vowels excised from context. >

132 citations


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