Approximating time-frequency density functions via optimal combinations of spectrograms

Loughlin, Patrick J.; Pitton, J.W.; Hannaford, Blake

doi:10.1109/97.338752

Cited by 48 publications

(42 citation statements)

References 9 publications

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“…However, five feature sets were compared. In [1], Loughlin et al proposed using a geometric mean of multiple spectrograms of different window sizes to overcome the time-frequency limitation of any single spectrogram. They showed that combining the information content from multiple spectrograms in form of their geometric mean, is optimal for minimizing the cross entropy between the multiple spectra.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Moreover, we show the relationship between the maximum likelihood QSS detection algorithm and the well known spectral matching property of the LP error measure [5]. Finally, we do a comparative study of the proposed variable-scale spectrum based features and the minimum cross-entropy time-frequency distributions developed by Loughlin et al [1].…”

Section: Introductionmentioning

confidence: 99%

“…We then formulate the problem of detecting QSSs as a Maximum Likelihood (ML) detection problem, defining a QSSs as the longest segment that has most probably been generated by the same AR process. 1 As is well known, given a p th order AR Gaussian QSS, the Minimum Mean Square Error (MMSE) linear prediction (LP) filter parameters [a(1), a(2), ... a(p)] are the most "compact" representation of that QSS amongst all the p th order all pole filters [9]. In other words, the normalized "coding error"…”

Section: Introductionmentioning

confidence: 99%

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On variable-scale piecewise stationary spectral analysis of speech signals for ASR

Tyagi

Bourlard

Wellekens

2006

Speech Communication

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Abstract. It is often acknowledged that speech signals contain short-term and long-term temporal properties [15] that are difficult to capture and model by using the usual fixed scale (typically 20ms) short time spectral analysis used in hidden Markov models (HMMs), based on piecewise stationarity and state conditional independence assumptions of acoustic vectors. For example, vowels are typically quasi-stationary over 40-80ms segments, while plosive typically require analysis below 20ms segments. Thus, fixed scale analysis is clearly sub-optimal for "optimal" timefrequency resolution and modeling of different stationary phones found in the speech signal. In the present paper, we investigate the potential advantages of using variable size analysis windows towards improving state-of-the-art speech recognition systems. Based on the usual assumption that the speech signal can be modeled by a time-varying autoregressive (AR) Gaussian process, we estimate the largest piecewise quasi-stationary speech segments, based on the likelihood that a segment was generated by the same AR process. This likelihood is estimated from the Linear Prediction (LP) residual error. Each of these quasi-stationary segments is then used as an analysis window from which spectral features are extracted. Such an approach thus results in a variable scale time spectral analysis, adaptively estimating the largest possible analysis window size such that the signal remains quasi-stationary, thus the best temporal/frequency resolution tradeoff. The speech recognition experiments on the OGI Numbers95 database [19], show that the proposed variable-scale piecewise stationary spectral analysis based features indeed yield improved recognition accuracy in clean conditions, compared to features based on minimum cross entropy spectrum [1] as well as those based on fixed scale spectral analysis. 2 IDIAP-RR 05-09

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On variable-scale piecewise stationary spectral analysis of speech signals for ASR

Tyagi

Bourlard

Wellekens

2006

Speech Communication

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“…Time-frequency density functions have been extensively studied [73] and have been applied in acoustics, radar, machine monitoring, and biomedical signal analysis as a framework for multiscale combinations of density-type functions, such as concentrations and energies, for example in examining molecular signal processing and detection in T-cells [74]. Multiscale time-frequency density functions have been previously proposed [75].…”

Section: E Detecting Inadequacies In the Validity Of A Model During mentioning

confidence: 99%

Strategies and Tactics in Multiscale Modeling of Cell-to-Organ Systems

2006

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Modeling is essential to integrating knowledge of human physiology. Comprehensive self-consistent descriptions expressed in quantitative mathematical form define working hypotheses in testable and reproducible form, and though such models are always "wrong" in the sense of being incomplete or partly incorrect, they provide a means of understanding a system and improving that understanding. Physiological systems, and models of them, encompass different levels of complexity. The lowest levels concern gene signaling and the regulation of transcription and translation, then biophysical and biochemical events at the protein level, and extend through the levels of cells, tissues and organs all the way to descriptions of integrated systems behavior. The highest levels of organization represent the dynamically varying interactions of billions of cells. Models of such systems are necessarily simplified to minimize computation and to emphasize the key factors defining system behavior; different model forms are thus often used to represent a system in different ways. Each simplification of lower level complicated function reduces the range of accurate operability at the higher level model, reducing robustness, the ability to respond correctly to dynamic changes in conditions. When conditions change so that the complexity reduction has resulted in the solution departing from the range of validity, detecting the deviation is critical, and requires special methods to enforce adapting the model formulation to alternative reduced-form modules or decomposing the reduced-form aggregates to the more detailed lower level modules to maintain appropriate behavior. The processes of error recognition, and of mapping between different levels of model complexity and shifting the levels of complexity of models in response to changing conditions, are essential for adaptive modeling and computer simulation of large-scale systems in reasonable time.

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“…Therefore, the pressure fluctuation should be controlled to improve the S characteristics. These fluctuations have been analyzed using time-frequency transformations in vassal studies [6,7].…”

Section: Introductionmentioning

confidence: 99%

Analysis of S Characteristics and Pressure Pulsations in a Pump-Turbine With Misaligned Guide Vanes

Sun¹,

Xiao

Liu

et al. 2013

Journal of Fluids Engineering

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Growing environmental concerns and the need for better power balancing and frequency control have increased attention in renewable energy sources such as the reversible pumpturbine which can provide both power generation and energy storage. Pump-turbine operation along the S-shaped curve can lead to difficulties in loading the rejection process with unusual increases in water pressure, which lead to machine vibrations. Pressure fluctuations are the primary reason for unstable operation of pump-turbines. Misaligned guide vanes (MGVs) are widely used to control the stability in the S region. There have been experimental investigations and computational fluid dynamics (CFD) simulations of scale models with aligned guide vanes and MGVs with spectral analyses of the S curve characteristics and the pressure pulsations in the frequency and time-frequency domains at runaway conditions. The course of the S characteristic is related to the centrifugal force and the large incident angle at low flow conditions with large vortices forming between the guide vanes and the blade inlets and strong flow recirculation inside the vaneless space as the main factors that lead to the S-shaped characteristics. Preopening some of the guide vanes enables the pump-turbine to avoid the influence of the S characteristic. However, the increase of the flow during runaway destroys the flow symmetry in the runner leading to all asymmetry forces on the runner that leads to hydraulic system oscillations. The MGV technique also increases the pressure fluctuations in the draft tube and has a negative impact on stable operation of the unit.

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Approximating time-frequency density functions via optimal combinations of spectrograms

Cited by 48 publications

References 9 publications

On variable-scale piecewise stationary spectral analysis of speech signals for ASR

On variable-scale piecewise stationary spectral analysis of speech signals for ASR

Strategies and Tactics in Multiscale Modeling of Cell-to-Organ Systems

Analysis of S Characteristics and Pressure Pulsations in a Pump-Turbine With Misaligned Guide Vanes

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