We propose a novel method for broadband noise reduction in speech, based upon a combined multiway subspace data reconstruction with adaptive signal enhancement. The multiway subspace method exploits a higher-order representation of the observed channel-signals in time-domain only. In the noise reduction context, multiway subspace data reconstruction is firstly performed using observed noisy signals, in order to estimate the overall shape of the original speech source signal. The enhanced speech signals are then obtained via post-processing by adaptive signal enhancers. Simulation studies show that the proposed two-staged approach can yield better noise reduction in terms of both objective and subjective performance metrics, compared to a widely used nonlinear spectral subtraction method and the method using sliding subspace projection.

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The aim of the article is to investigate an analytic function in one variable taking values close to integers at any positive integers. We obtain here refinements of numerical conditions so as the function to be a polynomial. We improve our previous results in [3], [4] such that several parameters are well balanced. Our method is based on computational Diophantine approximations with precise estimates in complex analysis.

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In recent years, it has been increased use of the open plan office in Japan. According to field surveys in Europe and the United States, noise is often the most detrimental factor of the indoor environment in open plan offices. In the workspace with no partitions, group workstations are often directly adjacent. Individuals can be disturbed by the intrusion of unwanted speech from nearby conversations which reduces productivity in the working environment. The purpose of this study was to indicate the acoustic condition both with background noise (air-conditioning noise) and unwanted noise interference from nearby workstations. Subjective judgement tests on desk work and objective measurements were conducted in a simulated office workplace. The results were as follows: The results of subjective judgement tests suggested that the degree of discomfort with hearing unwanted conversation are related to the speech level/background noise level (S/N(A)). However, within the S/N(A) is less than approximately 0 dB, the degree of discomfort with hearing unwanted conversation tend to be strongly affected by background noise. The Articulation Index (AI) is both shown to be well correlated with the degree of discomfort with hearing unwanted conversation and the task disturbance on desk work.

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