In this paper, we propose a new theory of nonlinear noise reduction with a perfectly musical-noise-free property, where no musical noise is generated even for a high signal-to-noise ratio. To achieve high-quality noise reduction with low musical noise, an iterative spectral subtraction method, i.e., recursively applied weak nonlinear signal processing, has been proposed. Although evaluation experiments indicated the existence of an appropriate parameter setting that gives a musical-noise-free state, no theoretical studies have been carried out. Therefore, in this paper, we theoretically derive pairs of internal parameters that satisfy the musical-noise-free condition by analysis based on higher-order statistics. It is clarified that finding a fixed point in the kurtosis of noise spectra enables the reproduction of the musical-noise-free state, and comparative experiments with commonly used noise reduction methods show the efficacy of the proposed method.

In this paper, we provide a theoretical analysis of the amount of musical noise in iterative spectral subtraction, and its optimization method for the least musical noise generation. To achieve high-quality noise reduction with low musical noise, iterative spectral subtraction, i.e., iteratively applied weak nonlinear signal processing, has been proposed. Although the effectiveness of the method has been reported experimentally, there have been no theoretical studies. Therefore, in this paper, we formulate the generation process of musical noise by tracing the change in kurtosis of noise spectra, and conduct a comparison of the amount of musical noise for different parameter settings but the same achieved level of noise attenuation. Furthermore, we theoretically derive the optimal internal parameters that generate no musical noise. It is clarified that to find a fixed point in kurtosis yields the no-musical-noise property. Comparative experiments with commonly used noise reduction methods show the proposed method's efficacy.

Recently, one of the authors has reported that the amount of generated musical noise is strongly correlated with higher-order statistics of the power spectra. On the basis of this finding, in this paper, we provide a new theoretical analysis of the amount of musical noise generated via the Wiener filtering family. Our theoretical analysis allows the universal performance description from the viewpoint of the amount of musical noise generation and that of noise reduction, enabling reasonable sound quality comparison under the same noise reduction performance. From a mathematical analysis and evaluation experiments, we also clarify which parameter settings result in less musical noise generated in the Wiener filtering family.

In this paper, we provide a new theoretical analysis of the amount of musical noise generated via generalized spectral subtraction based on higher order statistics. Power spectral subtraction is the most commonly used spectral subtraction method, and in our previous study a musical noise assessment theory limited to the power spectral domain was proposed. In this paper, we propose a generalization of our previous theory on spectral subtraction for arbitrary exponent parameters. We can thus compare the amount of musical noise between any exponent domains from the results of our analysis. We also clarify that less musical noise is generated when we choose a lower exponent spectral domain; this implies that there is no theoretical justification for using power/amplitude spectral subtraction.

In this paper, we propose a musical-noise-controllable algorithm for array signal processing with the aim for high-performance and high-quality noise reduction. Recently, many methods of integrating linear microphone array signal processing and nonlinear signal processing for noise reduction have been studied, but these methods often suffer from the problem of musical noise. In the proposed algorithm, channelwise spectral subtraction is applied before adaptive array signal processing. We also introduce a new automatic control algorithm to obtain the subtraction strength parameter used in the spectral subtraction, which depends on the amount of generated musical noise, measured by higher order statistics. We confirm the effectiveness of the proposed algorithm via objective and subjective evaluations.

In total knee joint replacement, the femur and the tibia must be precisely shaped to ensure that they firmly mate with the artificial joint. The normal procedure is to manually cut the bones using a bone saw and mechanical jigs. A 9-axis bone cutting machine tool has been developed by the authors to increase cutting accuracy and reduce the hospital stay required for recuperation. As developed, the cut plane is determined by a 5 degree-of-freedom mechanism and the femur and tibia are cut by moving two translational and one rotational degrees of freedom, a system that provides a cutter path which avoids cutting the ligament. A preoperative planning system based on CAD/CAM principles and an overview of a system for total knee arthroplasty is also discussed. Experimental results showed the effectiveness of the developed robot and the system.