Saliency detection for videos has been paid great attention and extensively studied in recent years. However, various visual scene with complicated motions leads to noticeable background noise and non-uniformly highlighting the foreground objects. In this paper, we proposed a video saliency detection model using spatio-temporal cues. In spatial domain, the location of foreground region is utilized as spatial cue to constrain the accumulation of contrast for background regions. In temporal domain, the spatial distribution of motion-similar regions is adopted as temporal cue to further suppress the background noise. Moreover, a backward matching based temporal prediction method is developed to adjust the temporal saliency according to its corresponding prediction from the previous frame, thus enforcing the consistency along time axis. The performance evaluation on several popular benchmark data sets validates that our approach outperforms existing state-of-the-arts.

The phenomenon of stochastic resonance (SR) in a mono-threshold-system-based detector (MTD) with additive background noise and multiplicative external noise is investigated. On the basis of maximum a posteriori probability (MAP) criterion, we deal with the binary signal transmission in four scenarios. The performance of the MTD is characterized by the probability of error detection, and the effects of system threshold and noise intensity on detectability are discussed in this paper. Similar to prior studies that focus on additive noises, along with increases in noise intensity, we also observe a non-monotone phenomenon in the multiplicative ways. However, unlike the case with the additive noise, optimal multiplicative noises all tend toward infinity for fixed additive noise intensities. The results of our model are potentially useful for the design of a sensor network and can help one to understand the biological mechanism of synaptic transmission.

This paper proposes a background noise estimation method using an outer product expansion with non-linear filters for ELF (extremely low frequency) electromagnetic (EM) waves. We proposed a novel source separation technique that uses a tensor product expansion. This signal separation technique means that the background noise, which is observed in almost all input signals, can be estimated using a tensor product expansion (TPE) where the absolute error (AE) is used as the error function, which is thus known as TPE-AE. TPE-AE has two problems: the first is that the results of TPE-AE are strongly affected by Gaussian random noise, and the second is that the estimated signal varies widely because of the random search. To solve these problems, an outer product expansion based on a modified trimmed mean (MTM) is proposed in this paper. The results show that this novel technique separates the background noise from the signal more accurately than conventional methods.

The observed phenomena in actual sound and electromagnetic environment are inevitably contaminated by the background noise of arbitrary distribution type. Therefore, in order to evaluate sound and electromagnetic environment, it is necessary to establish some signal processing methods to remove the undesirable effects of the background noise. In this paper, we propose noise cancellation methods for estimating a specific signal with the existence of background noise of non-Gaussian distribution from two viewpoins of static and dynamic signal processing. By applying the well-known least mean squared method for the moment statistics with several orders, practical methods for estimating the specific signal are derived. The effectiveness of the proposed theoretical methods is experimentally confirmed by applying them to estimation problems in actual sound and magnetic field environment.

We propose a semi-dynamic timing analysis flow applicable to large-scale circuits that takes into account dynamic power-supply drop. Logic delay is accurately estimated in the presence of power-supply noise through timing correction as a function of power-supply voltage during operation, where a time-dependent power-supply noise waveform is derived by way of a vectorless technique. Measurements and analysis of dynamic supply-noise waveforms and associated delay changes were performed on a sub-100-nm CMOS test circuit with embedded on-chip noise detectors and delay monitors. The proposed analysis technique was extended and applied to a test digital circuit with more than 10 million gates and validated toward a multi-10-million-gate CMOS SoC design.

Dynamic power supply noise measurements with resolutions of 100 ps and 100 µV for 100 ns and 1 V ranges are performed at various operating frequencies up to 400 MHz on multiple points in a low power register file and SRAM for product chips by using on-chip noise detectors. The measurements show that the noises are clearly emphasized in frequency domains by the interaction of circuit operations and bias network's AC transfers. A proposed design methodology that covers a fast SPICE simulator and parasitic extractors can predict dynamic noises from power supplies, ground, well, and substrate interactions to provide robustness to the design of low power body bias control circuitry.

This paper presents a newly developed noise post-processing (NPP) algorithm and the results of several tests demonstrating its subjective performance. This NPP algorithm is designed to improve the subjective performance of low bit-rate code excited linear prediction (CELP) decoding under background noise conditions. The NPP algorithm is based on a stationary noise generator and improves the subjective quality of noisy signal input. A backward adaptive detector defines noisy input signal frames from decoded LSF, energy, and pitch parameters. The noise generator estimates and produces stationary noise signals using past line spectral frequency (LSF) and energy parameters. The stationary noise generator has a frame erasure concealment (FEC) scheme designed for stationary noise signals and therefore improves the speech decoder's robustness for frame erasure under background noise conditions. The algorithm has been applied to the following CELP decoders: 1) a candidate algorithm of the ITU-T 4-kbit/s speech coding standard and 2) existing ITU-T standards, the G.729 and G.723.1 series. In both cases, NPP improved the subjective performance of the baseline decoders. Improvements of approximately 0.25 CMOS (CCR MOS: comparison category rating mean opinion score) and around 0.2-0.8 DMOS (DCR MOS: degradation category rating mean opinion score) were demonstrated in the results of our subjective tests when applied to the 4-kbit/s decoder and G.729/G.723.1 decoders respectively. Other test results show that NPP improves the subjective performance of a G.729 decoder by around 0.45 in DMOS under both error-free and frame-erasure conditions, and a further improvement of around 0.2 DMOS is achieved by the FEC scheme in the noise generator.

In this paper, first, we consider how to illustrate the effect of background noise to the measurement of room acoustics under a background noise of arbitrary distribution type. Two kinds of estimation methods are proposed to evaluate a proper reverberation time of a room by observing real unrefined decay curves, which can not realize smoothly a sufficient decay of 60 dB in a low frequency region, especially under a contamination of background noise. In the first method, an observation equation is derived from a stochastic model by means of well-known Sabine's differential equation, which is approximately rewritten in a matched form of difference equation especially to preserve its original physical meaning and functional linearity on the reverberation parameter. The effect of background noise is eliminated by employing a generalized state estimation algorithm based on Bayes' theorem. In the second one, after reflecting the effect of background noise in an observation equation of measuring model, a well-known mutual information criterion is introduced to estimate a reverberation time especially based on the basic property of statistical independency between signal and background noise. Finally, the effectiveness of the proposed methods are experimentally confirmed too by applying it to the actual measurement of a reverberation time in the actual living situation of room contaminated by a background noise. The proposed methods are, however, some technique using actively the higher order correlation beyond a linear one, and so they are methodology-trials which should coexist with other techniques.

The processes observed in a sound environment inevitably contain additional external noise of arbitrary distribution. Furthermore, the actual sound environment system exhibits various types of linear and non-linear characteristics, and it often contains an unknown structure. In this paper, a method for estimating the input signal for a sound environment system with unknown structure and additive noise of arbitrary probability distribution is proposed by introducing a system model of the conditional probability type. The effectiveness of the proposed theoretical method is confirmed experimentally by applying it to the actual problem of input estimation of the sound environment.

Voice activity detection (VAD) is to determine whether a short time speech frame is voice or silence. VAD is useful in reducing the mean speech coding rate by suppressing transmission during silence periods, and is effective in transmitting speech and other data simultaneously. This letter describes a VAD system that uses a neural network. The neural network gets several parameters by analyzing slices of the speech wave form, and outputs only one scalar value related to voice activity. This output is compared to a threshold to determine whether the slice is voice or silence. The mean code transfer rate can be reduced to less than 50% by using the proposed VAD system.

This paper describes a trial of evaluating the proper characteristics of multiple sound insulatain systems from their output responses contaminated by unknown background noises. The unknown parameters of sound insulation systems are first estimated on the basis of hte linear time series on an intensity scale, describing functionally the input-output relation of the systems. Then, their output probability distributions are predicted when an arbitrary input noise passes through these insulation systems.

This paper describes a new trial of dynamical parameter estimation for the actual room acoustic system, in a practical case when the input excitation is polluted by a background noise in contrast with the usual case when the output observation is polluted. The room acoustic system is first formulated as a discrete time model, by taking into consideration the original standpoint defining the system parameter and the existence of the background noise polluting the input excitation. Then, the recurrence estimation algorithm on a reverberation time of room is dynamically derived from Bayesian viewpoint (based on the statistical information of background noise and instantaneously observed data), which is applicable to the actual situation with the non-Gaussian type sound fluctuation, the non-linear observation, and the input background noise. Finally, the theoretical result is experimentally confirmed by applying it to the actual estimation problem of a reverberation time.