Koji Sakamoto

This paper proposes a binaural model for estimating the sound image width of the audio-listening environment in a car cabin. Based on a binaural model for the sound image azimuth estimation proposed in a previous study, a new feature was extracted and expanded into a model capable of estimating the sound image width. To improve the estimation accuracy of this model, the width of the sound image azimuth estimation distribution was extracted from the attack region of the gammachirp auditory filter bank outputs, and a multiple nonlinear regression model that uses the feature and sound image azimuth estimate value was constructed and examined. As the results indicate, the proposed method achieves a significantly higher accuracy.

An azimuth estimation binaural model of sound image for an audio listening environment in a car cabin is proposed. To improve the estimation accuracy of this model, a gammachirp auditory filterbank was used to extract the physical characteristics related to the in-cabin audio listening environment sound image azimuth. The effectiveness of the proposed method was confirmed by its higher level of correlation between the experimental and estimated values as compared to the frequency domain binaural model.