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.

Speech enhancement based on audible noise suppression

Abstract: A novel speech enhancement technique is presented based on the definition of the psychoacoustically derived quantity of audible noise spectrum and its subsequent suppression using optimal nonlinear filtering of the short-time spectral amplitude (STSA) envelope. The filter operates with sparse spectral estimates obtained from the STSA, and, when these parameters are accurately known, significant intelligibility gains, up to 40%, result in the processed speech signal. These parameters can be also estimated from noisy data, resulting into smaller but significant intelligibility gains.

Common Mode EMI Noise Suppression for Bridgeless PFC Converters

Abstract: The goal of this paper is to study the possibility of minimizing common mode (CM) noise emission in bridgeless power factor correction (PFC) converters. Two approaches are proposed. In the first approach, the bridgeless PFC is modified to achieve symmetry. A CM noise model for symmetric topology is derived and the conditions for symmetry are summarized. Parasitics critical to the symmetrical condition are studied and carefully controlled. As a result, CM noise can be minimized with good cancellation. The second approach is to introduce a balance technique to bridgeless PFC converters. The topology is modified so that the balance technique can be applied so as to minimize CM noise. Experimental results validate that both approaches can greatly reduce CM noise up to 30 dBmuV. The two approaches are compared in terms of both its effects on CM noise and their implementations.

Robust MVDR beamforming using time-frequency masks for online/offline ASR in noise

Abstract: This paper considers acoustic beamforming for noise robust automatic speech recognition (ASR). A beamformer attenuates background noise by enhancing sound components coming from a direction specified by a steering vector. Hence, accurate steering vector estimation is paramount for successful noise reduction. Recently, a beamforming approach was proposed that employs time-frequency masks. In the speech recognition system we submitted to the CHiME-3 Challenge, we employed a new form of this approach that uses a speech spectral model based on a complex Gaussian mixture model (CGMM) to estimate the time-frequency masks and the steering vector without providing technical details. This paper elaborates on this technique and examines its effectiveness for ASR. Experimental results show that the CGMM-based approach outperforms a recently proposed mask estimator based on a Watson mixture model. In addition, the CGMM-based approach is extended to an online speech enhancement scenario, which allows this technique to be used in an online recognition setup. This online version reduces the CHiME-3 evaluation error rate from 15.60% to 8.47%, which is a comparable improvement to that obtained by batch processing.

Comparison of PDE-Based Nonlinear Diffusion Approaches for Image Enhancement and Denoising in Optical Coherence Tomography

Abstract: A comparison between two nonlinear diffusion methods for denoising OCT images is performed. Specifically, we compare and contrast the performance of the traditional nonlinear Perona-Malik filter with a complex diffusion filter that has been recently introduced by Gilboa . The complex diffusion approach based on the generalization of the nonlinear scale space to the complex domain by combining the diffusion and the free Schrodinger equation is evaluated on synthetic images and also on representative OCT images at various noise levels. The performance improvement over the traditional nonlinear Perona-Malik filter is quantified in terms of noise suppression, image structural preservation and visual quality. An average signal-to-noise ratio (SNR) improvement of about 2.5 times and an average contrast to noise ratio (CNR) improvement of 49% was obtained while mean structure similarity (MSSIM) was practically not degraded after denoising. The nonlinear complex diffusion filtering can be applied with success to many OCT imaging applications. In summary, the numerical values of the image quality metrics along with the qualitative analysis results indicated the good feature preservation performance of the complex diffusion process, as desired for better diagnosis in medical imaging processing

A new variable step size LMS algorithm-based method for improved online secondary path modeling in active noise control systems

Abstract: This paper proposes a new method for online secondary path modeling in active noise control systems. The existing methods for active noise control systems with online secondary path modeling consist of three adaptive filters. The main feature of the proposed method is that it uses only two adaptive filters. In the proposed method, the modified-FxLMS (MFxLMS) algorithm is used in adapting the noise control filter and a new variable step size (VSS) least mean square (LMS) algorithm is proposed for adaptation of the secondary path modeling filter. This VSS LMS algorithm is different from the normalized-LMS (NLMS) algorithm, where the step size is varied in accordance with the power of the reference signal. Here, on the other hand, the step size is varied in accordance with the power of the disturbance signal in the desired response of the modeling filter. The basic idea of the proposed VSS algorithm stems from the fact that the disturbance signal in the desired response of the modeling filter is decreasing in nature, (ideally) converging to zero. Hence, a small step size is used initially and later its value is increased accordingly. The disturbance signal, however, is not available directly, and we propose an indirect method to track its variations. Computer simulations show that the proposed method gives better performance than the existing methods. This improved performance is achieved at the cost of a slightly increased computational complexity.