Musical Noise Generation Research Articles

Musical-noise-free blind speech extraction integrating microphone array and iterative spectral subtraction

In this paper, we propose a musical-noise-free blind speech extraction method using a microphone array for application to nonstationary noise. In our previous study, it was found that optimized iterative spectral subtraction (SS) results in speech enhancement with almost no musical noise generation, but this method is valid only for stationary noise. The proposed method consists of iterative blind dynamic noise estimation by, e.g., independent component analysis (ICA) or multichannel Wiener filtering, and musical-noise-free speech extraction by modified iterative SS, where multiple iterative SS is applied to each channel while maintaining the multichannel property reused for the dynamic noise estimators. Also, in relation to the proposed method, we discuss the justification of applying ICA to signals nonlinearly distorted by SS. From objective and subjective evaluations simulating a real-world hands-free speech communication system, we reveal that the proposed method outperforms the conventional methods.

Musical-Noise-Free Speech Enhancement Based on Optimized Iterative Spectral Subtraction

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.

Audio noise reduction utilizing inharmonic‐frequency analysis of generalized harmonic analysis (GHA)

Audio noise reduction is a desired technology for better telecommunications. Currently, the technology is based mostly upon spectral subtraction methods. However, its fundamental problem of musical noise generation remains unsolved. Musical noise is produced by mismatching of instantaneous phases of presampled noise and target noise to be canceled, which causes noise‐breathing effects synchronous with the object’s intensity variation. Additionally, unnecessary frequency components inherent to Fourier analysis to resynthesis process are mixed in the processed output. For this reason, spectral subtraction is inapplicable to high‐fidelity audio. The authors have devoted attention to inharmonic frequency analysis of generalized harmonic analysis (GHA), which was originally proposed by Wiener. The analyses were intended to examine stochastic signals utilizing harmonic frequency analysis. Results clarified that any signal can be represented as an almost periodic function whose frequency components have an inharmonic relationship. In 1993, Dr. Hirata proposed inharmonic frequency analysis based upon successive matching with frequency component templates, which is applicable to high‐fidelity audio signal processing. This method came to be called GHA, following Wiener’s theory. The authors mostly attempted noise reduction in damaged SP records of historical value, retrieving almost noise‐free musical sounds.