Noise reduction

About: Noise reduction is a research topic. Over the lifetime, 25121 publications have been published within this topic receiving 300815 citations. The topic is also known as: denoising & noise removal.

Robust scanner identification based on noise features

Abstract: A large portion of digital image data available today is acquired using digital cameras or scanners While cameras allow digital reproduction of natural scenes, scanners are often used to capture hardcopy art in more controlled scenarios This paper proposes a new technique for non-intrusive scanner model identification, which can be further extended to perform tampering detection on scanned images Using only scanned image samples that contain arbitrary content, we construct a robust scanner identifier to determine the brand/model of the scanner used to capture each scanned image The proposed scanner identifier is based on statistical features of scanning noise We first analyze scanning noise from several angles, including through image de-noising, wavelet analysis, and neighborhood prediction, and then obtain statistical features from each characterization Experimental results demonstrate that the proposed method can effectively identify the correct scanner brands/models with high accuracy

Blind estimation of the coherent-to-diffuse energy ratio from noisy speech signals

Abstract: A novel dual-channel algorithm is proposed which estimates the coherent-to-diffuse energy ratio (CDR) of background noise in mixed noise fields. The algorithm is based on an estimate of the noise field coherence from a noisy speech signal and a subsequent minima tracking in order to increase the estimation accuracy even in the presence of speech. The obtained CDR estimate can be used, e.g., for the acoustic environment classification in hearing aids or to control speech enhancement algorithms such as noise reduction or speech dereverberation. Besides, the approach can be used to calculate an estimate of the direct-to-reverberant energy ratio (DRR) blindly from reverberant speech signals.

PCA detection and denoising of Zeeman signatures in stellar polarised spectra

Abstract: Our main objective is to develop a denoising strategy to increase the signal to noise ratio of individual spectral lines of stellar spectropolarimetric observations. We use a multivariate statistics technique called Principal Component Analysis. The cross-product matrix of the observations is diagonalized to obtain the eigenvectors in which the original observations can be developed. This basis is such that the first eigenvectors contain the greatest variance. Assuming that the noise is uncorrelated a denoising is possible by reconstructing the data with a truncated basis. We propose a method to identify the number of eigenvectors for an efficient noise filtering. Numerical simulations are used to demonstrate that an important increase of the signal to noise ratio per spectral line is possible using PCA denoising techniques. It can be also applied for detection of magnetic fields in stellar atmospheres. We analyze the relation between PCA and commonly used well-known techniques like line addition and least-squares deconvolution. Moreover, PCA is very robust and easy to compute.

Noise reduction apparatus and audio reproduction apparatus

Abstract: A noise reduction apparatus includes: a speaker with a speaker unit held by holding means to make it possible to mix sounds emitted from front and rear of a vibration plate of the speaker; a microphone provided in an area where the sounds emitted from the front and rear of the vibration plate of the speaker are mixed and cancelled; and means for supplying a noise reduction audio signal obtained by phase-inverting an audio signal collected by the microphone to the speaker.

Noise-reduction system

Abstract: A noise-suppression circuit (10) divides the signal from a microphone (12) into a plurality of frequency sub-bands by means of a noise-band divider (18) and a subtraction circuit (36). By means of gain circuits (32) and (34), it applies separate gains to the separate bands and then recombines them in a signal combiner (38) to generate an output signal in which the noise has been suppressed. Separate gains are applied only to the lower subbands in the voice spectrum. Accordingly, the noise-band divider (18) is required to compute spectral components for only those bands. By employing a sliding-discrete-Fourier-transform method, the noise-band divider (18) computes the spectral components on a sample-by-sample basis, and circuitry (50, 52) for determining the individual gains can therefore update them on a sample-by-sample basis, too.