On the detection of transient signals using spectral correlation

Baugh, K.W.; Hardwicke, Keith R.

doi:10.1007/bf01183742

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…Also, since the nonstationary signals show strong correlations, the cross-PSD of the GSC output is significantly larger in NTFBs than in stationary noise-only TFBs. Hence, we define a detection statistic, D, for the nonstationary signal component of the GSC output [16] as…”

Section: Detection Of Ntfbs and Psd Estimation Of Stationary Noisementioning

confidence: 99%

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Two-Microphone Generalized Sidelobe Canceller with Post-Filter Based Speech Enhancement in Composite Noise

Park

Kim

Han

et al. 2016

ETRI J

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This paper describes an algorithm to suppress composite noise in a two‐microphone speech enhancement system for robust hands‐free speech communication. The proposed algorithm has four stages. The first stage estimates the power spectral density of the residual stationary noise, which is based on the detection of nonstationary signal‐dominant time‐frequency bins (TFBs) at the generalized sidelobe canceller output. Second, speech‐dominant TFBs are identified among the previously detected nonstationary signal‐dominant TFBs, and power spectral densities of speech and residual nonstationary noise are estimated. In the final stage, the bin‐wise output signal‐to‐noise ratio is obtained with these power estimates and a Wiener post‐filter is constructed to attenuate the residual noise. Compared to the conventional beamforming and post‐filter algorithms, the proposed speech enhancement algorithm shows significant performance improvement in terms of perceptual evaluation of speech quality.

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Section: Detection Of Ntfbs and Psd Estimation Of Stationary Noisementioning

confidence: 99%

“…Therefore, (17) can be easily derived from (15) and (16). Then, R(τ, k) values have the following relations according to the TFB class of (9):…”

Section: Detection Of Speech-dominant Tfbsmentioning

confidence: 99%

Two-Microphone Generalized Sidelobe Canceller with Post-Filter Based Speech Enhancement in Composite Noise

Park

Kim

Han

et al. 2016

ETRI J

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“…1) For the Transient Noise Components, for and a Small Value of , where : By using the transient noise model presented in [15, (2.1)], the two conditions of property 4 can be derived from [15, (2.12)] directly. Notice should be given that [15, (2.12)] holds true only in the region defined by: (14) where is the center frequency of a stable, unit gain time-invariant bandpass filter as defined in [15] and is the bandwidth. Generally, the bandwidth is not a large value, so should not be too large to ensure that (14) holds true.…”

Section: Statistical Properties Of the Gas-cfmentioning

confidence: 99%

“…2) For the Howling Components, for and Arbitrary Values of : It is well-known that the closedloop system becomes unstable if and only if the following two conditions occur [16]: (15) where and are, respectively, the short-term frequency responses of the forward and feedback path at in the th frame. When (15) holds true, an oscillation at will occur, especially when the forward path and the feedback path are time-invariant [16]. In other words, the howling components are always pure oscillations, which makes it have an infinite coherence times for these howling components.…”

Section: Statistical Properties Of the Gas-cfmentioning

confidence: 99%

On Generalized Auto-Spectral Coherence Function and Its Applications to Signal Detection

Zheng

Yang

2014

IEEE Signal Process. Lett.

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Considering that spectral components of one random process are not necessarily independent for all types of signals, this paper defines a generalized auto-spectral coherence function (GAS-CF) to measure this spectral correlation. The GAS-CF is a generalization of the temporal coherence function and the spectral coherence function, where they have already been successfully applied to detect howling components and transient noise components, respectively. After defining the GAS-CF, this paper studies its statistical properties in detail. Simulation results show that the proposed GAS-CF can be applied to detect different types of signals, including transient noise, howling frequency and chirp signal, in a simple way.

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“…Higher order techniques show promise in applications for stationary signals and also for short-time transients where only a single occurrence of a signal may be available for detection ͑Dwyer, 1984; Hinich and Wilson, 1990;Kletter and Messer, 1990;Sangfelt and Persson, 1993;Delaney, 1994;Tague et al, 1994;Baugh and Hardwicke, 1994;Nuttall, 1994͒. The latter case, for correlation detectors, has been investigated in previous papers by the authors using both computer simulations ͑Pflug et al., 1992b, 1994b͒ and more recently for unknown source detection, using theoretical performance predictions for the case of uncorrelated noise ͑Pflug et al, 1995b͒.…”

Section: Introductionmentioning

confidence: 99%

Known source detection predictions for higher order correlators

Pflug

Ioup

1998

The Journal of the Acoustical Society of America

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The problem addressed in this paper is whether higher order correlation detectors can perform better in white noise than the cross correlation detector for the detection of a known transient source signal, if additional receiver information is included in the higher order correlations. While the cross correlation is the optimal linear detector for white noise, additional receiver information in the higher order correlations makes them nonlinear. In this paper, formulas that predict the performance of higher order correlation detectors of energy signals are derived for a known source signal. Given the first through fourth order signal moments and the noise variance, the formulas predict the SNR for which the detectors achieve a probability of detection of 0.5 for any level of false alarm, when noise at each receiver is independent and identically distributed. Results show that the performance of the cross correlation, bicorrelation, and tricorrelation detectors are proportional to the second, fourth, and sixth roots of the sampling interval, respectively, but do not depend on the observation time. Also, the SNR gains of the higher order correlation detectors relative to the cross correlation detector improve with decreasing probability of false alarm. The source signal may be repeated in higher order correlations, and gain formulas are derived for these cases as well. Computer simulations with several test signals are compared to the performance predictions of the formulas. The breakdown of the assumptions for signals with too few sample points is discussed, as are limitations on the design of signals for improved higher order gain. Results indicate that in white noise it is difficult for the higher order correlation detectors in a straightforward application to achieve better performance than the cross correlation. ͓S0001-4966͑98͒01805-0͔

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On the detection of transient signals using spectral correlation

Cited by 9 publications

References 7 publications

Two-Microphone Generalized Sidelobe Canceller with Post-Filter Based Speech Enhancement in Composite Noise

Two-Microphone Generalized Sidelobe Canceller with Post-Filter Based Speech Enhancement in Composite Noise

On Generalized Auto-Spectral Coherence Function and Its Applications to Signal Detection

Known source detection predictions for higher order correlators

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