Spectrogram

About: Spectrogram is a research topic. Over the lifetime, 5813 publications have been published within this topic receiving 81547 citations.

Control method and device for engine

Abstract: A control method and a control device of an engine are introduced. During the process of the engine controlling, the control unit does the adaptive learning for the actual target value of the feedback of different aims, and following the dynamic spectrogram generation strategy optimizing compares the adaptively learning parameter of the same working status and the same time with the basic spectrogram parameter. If the compared result doesn't meet the condition, then keep the basic spectrogram parameter. And if it meets the condition, then the engine generates the dynamic spectrogram parameter. Based on the dynamic spectrogram combination strategy, the engine combines the basic spectrogram parameter and the dynamic spectrogram parameter generated to the combined spectrogram parameter instead of the basic spectrogram parameter.

Mechanism of the cross-terms in spectrograms

Abstract: Using the ambiguity domain interpretation, the authors explore the mechanism of the cross-terms (or interferences) in spectrograms. They emphasize that the spectrogram does, in general, have cross-terms. Depending on the signal structure and the window used, the cross-terms may sometimes be conspicuous, reduced, or completely eliminated. The cross-term suppression in spectrograms is governed by the signal-theoretic version of the uncertainty principle. Some experimental results are provided to illustrate the cross-term mechanism in spectrograms. >

Vision perceptually restores auditory spectral dynamics in speech.

Abstract: Visual speech facilitates auditory speech perception, but the visual cues responsible for these benefits and the information they provide remain unclear. Low-level models emphasize basic temporal cues provided by mouth movements, but these impoverished signals may not fully account for the richness of auditory information provided by visual speech. High-level models posit interactions among abstract categorical (i.e., phonemes/visemes) or amodal (e.g., articulatory) speech representations, but require lossy remapping of speech signals onto abstracted representations. Because visible articulators shape the spectral content of speech, we hypothesized that the perceptual system might exploit natural correlations between midlevel visual (oral deformations) and auditory speech features (frequency modulations) to extract detailed spectrotemporal information from visual speech without employing high-level abstractions. Consistent with this hypothesis, we found that the time–frequency dynamics of oral resonances (formants) could be predicted with unexpectedly high precision from the changing shape of the mouth during speech. When isolated from other speech cues, speech-based shape deformations improved perceptual sensitivity for corresponding frequency modulations, suggesting that listeners could exploit this cross-modal correspondence to facilitate perception. To test whether this type of correspondence could improve speech comprehension, we selectively degraded the spectral or temporal dimensions of auditory sentence spectrograms to assess how well visual speech facilitated comprehension under each degradation condition. Visual speech produced drastically larger enhancements during spectral degradation, suggesting a condition-specific facilitation effect driven by cross-modal recovery of auditory speech spectra. The perceptual system may therefore use audiovisual correlations rooted in oral acoustics to extract detailed spectrotemporal information from visual speech.

Spectral Clustering for Speech Separation

Abstract: Spectral clustering refers to a class of recent techniques which rely on the eigenstructure of a similarity matrix to partition points into disjoint clusters, with points in the same cluster having high similarity and points in different clusters having low similarity. In this chapter, we introduce the main concepts and algorithms together with recent advances in learning the similarity matrix from data. The techniques are illustrated on the blind one-microphone speech separation problem, by casting the problem as one of segmentation of the spectrogram.