A noise-estimation algorithm is proposed for highly non-stationary noise environments. The noise estimate is updated by averaging the noisy speech power spectrum using time and frequency dependent smoothing factors, which are adjusted based on signal-presence probability in individual frequency bins. Signal presence is determined by computing the ratio of the noisy speech power spectrum to its ...

Noise power spectral density (PSD) estimation is a crucial part of speech enhancement system due to its contributory effect on the quality of the noise reduced speech. A novel estimation method for color noise PSD on the basis of an assumption of generalized Gamma distribution and maximum a posteriori (MAP) criterion is proposed. In the experiment, generalized Gamma PDF which is a natural exten...

In this paper a new noise spectrum estimation algorithm is described for single-channel acoustic noise suppression systems. To achieve fast convergence during abrupt change of noise floor, the proposed algorithm uses a minimum correction module to adjust an adaptive noise estimator. The minimum search duration is controlled by a harmonicity module for improved noise tracking under continuous vo...

Abstract In a dataset, the misidentified labels can be assumed as true flipped with probability. this paper, we study general situation in which sample are corrupted at random. We propose noise rate estimation method and prove that by adopting importance reweighting, accuracy of classification label problem rise approximately 10% through any surrogate loss function. The two methods choose for r...

This paper examines whether non-acoustic noise reference signals can provide accurate estimates of noise at very low signalto-noise ratios (SNRs) where conventional estimation methods are less effective. The environment chosen for the investigation is Formula 1 motor racing where SNRs are as low as -15dB and the non-acoustic reference signals are engine speed, road speed and throttle measuremen...

A second-generation wavelet based implementation of two adaptive noise estimation algorithms, which do not require explicit use of voice activity detector or signal statistics learning process, is introduced. The first algorithm utilises a smoothing parameter based on estimation of the wavelet subbands signal-to-noise ratio of the signal. The second algorithm is based on tracking the minimum va...

In the paper, we present a new noise spectrum estimation algorithm which is simple and effective for non-stationary background noise environments. The new proposed algorithm continuously updates the estimated noise by weighted noisy speech with a constant smoothing factor, the weighting factor is adjusted by an estimated signal-tonoise ratio (SNR), and the SNR is controlled by the local energy ...

This paper describes a path toward the development of theory for using a low noise high frame rate camera as a star tracker for spacecraft attitude estimation. The benefit of using a low noise high frame rate camera is that star data can be sampled at a faster rate while allowing one to measure very dim stars, increasing the number of stars available for attitude estimation. The development of ...

Tracking error estimation is of great importance in global navigation satellite system (GNSS) receivers. Any inaccurate estimation for tracking error will decrease the signal tracking ability of signal tracking loops and the accuracies of position fixing, velocity determination, and timing. Tracking error estimation can be done by traditional discriminator, or Kalman filter-based pre-filter. Th...

the volterra model is widely used for nonlinearity identification in practical applications. in this paper, we employed volterra model to find the nonlinearity relation between electroencephalogram (eeg) signal and the noise that is a novel approach to estimate noise in eeg signal. we show that by employing this method. we can considerably improve the signal to noise ratio by the ratio of at le...