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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.


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Journal ArticleDOI
TL;DR: A time-frequency analysis (TFA) is proposed to derive the backscattering properties of each pixel in single-polarization synthetic aperture radar (SAR) images, which represents another way of characterizing the physical mechanisms involved in image formation.
Abstract: In this paper, a time-frequency analysis (TFA) is proposed to derive the backscattering properties of each pixel in single-polarization synthetic aperture radar (SAR) images. At high resolution (HR), some backscattering variations which are linked to the scene geometry and the surface property occur during the radar acquisition. TFA permits to retrieve these variations from the synthesized images. The proposed TFA algorithm is based on a sliding bandpass filtering in the Fourier domain, from which a spectrogram featuring the range and azimuth backscattering variations is derived. The spectrograms summarize the physical properties of each pixel. From the spectrogram analysis, four target classes representing the four main kinds of backscattering behaviors observed in SAR images are defined: frequency invariant, range variant, azimuth variant, and 2-D variant. These classes can further be linked to the physical properties of the objects. An original and simple set of five features estimated from spectrograms is proposed to classify point targets into these four classes. A performance assessment of this classification is carried out, using ONERA/RAMSES X-band airborne images acquired over the city of Toulouse, France. A robustness analysis is also conducted, in order to assess the impact of incidence angle and resolution on the classification performance. Finally, results are also given for spaceborne images (TerraSAR-X spotlight images). The physical interpretation developed in airborne case appears to be also valid for metric spaceborne data. After studying the TFA on HR spaceborne images, the tradeoff between HR coupled with TFA and medium resolution coupled with polarimetric analysis is investigated. Actually, TFA represents another way of characterizing the physical mechanisms involved in image formation.

35 citations

Journal ArticleDOI
TL;DR: This paper proposes a frame-wise classification framework to process full breathing cycles of multi-channel lung sound recordings with a convolutional recurrent neural network, and outperforms the other networks by achieving an F-score of F1≈92%.

35 citations

Proceedings Article
01 Jan 2000
TL;DR: A new mask estimation technique is presented that uses a Bayesian classifier to determine the reliability of spectrographic elements and resulted in significantly better recognition accuracy than conventional mask estimation methods.
Abstract: Missing feature methods of noise compensation for speech recognition operate by removing components of a spectrographic representation of speech that are considered to be corrupt, as indicated by a low signal-to-noise ratio. Recognition is either performed directly on the incomplete spectrograms or the missing components are reconstructed prior to recognition. These methods require a spectrographic mask which accurately labels the reliable and corrupt regions of the spectrogram. Current methods of mask estimation rely on assumptions about the corrupting noise such as stationarity. This is a significant drawback since the missing feature methods themselves have no such restrictions. We present a new mask estimation technique that uses a Bayesian classifier to determine the reliability of spectrographic elements. Features were designed that make no assumptions about the corrupting noise signal, but rather exploit characteristics of the speech signal itself. Missing feature compensation experiments were performed on speech corrupted by a variety of noises. In all cases, classifier-based mask estimation resulted in significantly better recognition accuracy than conventional mask estimation methods.

35 citations

Journal ArticleDOI
04 Jun 1996
TL;DR: In this article, Tikhonov deconvolution is used for transforming the processed spectrogram in such a way as to facilitate finding initial estimates of its parameters, i.e., gains in accuracy of estimating the parameters of peaks, are demonstrated using both synthetic and real-world spectrophotometric data.
Abstract: The problem of spectrogram interpretation is considered under the assumption that the parameters of spectral peaks-their positions and magnitudes-contain the information essential for spectrometric analysis. The subsequent use of Tikhonov deconvolution and iterative correction of the estimates of those parameters is proposed. Deconvolution is used for transforming the processed spectrogram in such a way as to facilitate finding initial estimates of its parameters. The advantages of the proposed approach, i.e., gains in accuracy of estimating the parameters of peaks, are demonstrated using both synthetic and real-world spectrophotometric data.

35 citations

Posted Content
TL;DR: This work presents a brief history of work on the method it will be called the method of time-frequency reassignment, and presents a unified mathematical description of the technique and its derivation.
Abstract: Time-frequency representations such as the spectrogram are commonly used to analyze signals having a time-varying distribution of spectral energy, but the spectrogram is constrained by an unfortunate tradeoff between resolution in time and frequency. A method of achieving high-resolution spectral representations has been independently introduced by several parties. The technique has been variously named reassignment and remapping, but while the implementations have differed in details, they are all based on the same theoretical and mathematical foundation. In this work, we present a brief history of work on the method we will call the method of time-frequency reassignment, and present a unified mathematical description of the technique and its derivation. We will focus on the development of time-frequency reassignment in the context of the spectrogram, and conclude with a discussion of some current applications of the reassigned spectrogram.

35 citations


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