A vibroacoustic numerical method employing an implicit finite-difference time-domain (FDTD) method, in which the target architecture is modeled as a composition of two-dimensional plate elements, is proposed in this paper. While structure-borne sound is a difficult phenomenon to predict owing to the complexity of the vibration mechanism on the building structure, wave-based numerical techniques may enable its accurate prediction by virtue of their flexibility from the viewpoint of modeling the object. However, with the current PC performance, prediction for a large-scale problem is still difficult. To solve such a problem, we model the target structure as a composition of plate elements to reduce the simulated field to two dimensions, in contrast to the discretization of the field into three-dimensional solid elements. This results in memorysaving and faster simulation. In this paper, the basic theory of vibroacoustic analysis for a model with plate elements is described, and the results of a case study for a box-type structure are discussed.

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A vibration analysis method for structure-borne sound transmission in framed structures using an implicit finite-difference time-domain (FDTD) method is proposed in this paper. The prediction of structure-borne sound is difficult owing to the complexity of the vibration mechanism in building structures. As a powerful means of analyzing structure-borne sound, wave-based numerical techniques have the potential to solve the problem accurately by virtue of their flexibility from the viewpoint of modeling the object. For this reason, we model the target structure as a composition of beam elements and calculate the characteristics of the wave propagation using the FDTD method. Using the beam model, we can decrease the dimension of the simulated field to one dimension, compared with the situation that the field is discretized by three-dimensional solid elements. This results in a reduction of the computational cost. In this paper, the basic theory of the calculation method for a model with beam elements is described and the results of a case study of a multilayered frame structure are discussed.

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This paper introduces a frequency-domain acoustic echo reduction process based on a new Wiener-filtering method taking into account the cross-spectral term between the acoustic echo and the near-end speech. The conventional echo reduction method based on Wiener filtering estimates the gain based on the assumption that the cross-spectral term of the echo and the near-end speech is zero because the acoustic echo and the near-end speech are statistically uncorrelated. However, this assumption does not always hold true in practice because the gain is estimated in a very short period where the amount of statistical data, which is used to calculate the ensemble averages of the observed signals, is insufficient. As a result, the conventional method occasionally causes the perceptual degradation of sound quality during a double-talk situation; therefore, the performance is still not sufficient. Our goal was to accurately calculate the echo-reduction gain to decrease the speech distortions produced by the echo-reduction process. The proposed method solves a least mean square error of the Wiener-filtering method by taking into account the cross-spectral term between the echo and the near-end speech to obtain a better echo-reduction gain. The performance of this method was demonstrated by objective and subjective results in which speech distortions were decreased.

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In this paper, we describe a dataset of head-related transfer functions (HRTFs) measured at the Research Institute of Electrical Communications, Tohoku University. The current dataset includes HRTFs for 105 subjects at 72 azimuths × 13 elevations of spherical coordinates. Anthropometric data for 39 subjects are also included. The measurement and postprocessing methods are outlined in this paper. These data will be freely accessible for nonprofit academic purposes via the Internet. Moreover, this dataset will be included in an international joint project to gather several HRTF datasets in a unified data format.

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