• International Journal of Internet, Broadcasting and Communication
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• 제13권1호
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• pp.161-167
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• 2021

This paper proposes a new personalized HRTF estimation method which is based on a deep neural network (DNN) model and improved elevation reproduction using a notch filter. In the previous study, a DNN model was proposed that estimates the magnitude of HRTF by using anthropometric measurements [1]. However, since this method uses zero-phase without estimating the phase, it causes the internalization (i.e., the inside-the-head localization) of sound when listening the spatial sound. We devise a method to estimate both the magnitude and phase of HRTF based on the DNN model. Personalized HRIR was estimated using the anthropometric measurements including detailed data of the head, torso, shoulders and ears as inputs for the DNN model. After that, the estimated HRIR was filtered with an appropriate notch filter to improve elevation reproduction. In order to evaluate the performance, both of the objective and subjective evaluations are conducted. For the objective evaluation, the root mean square error (RMSE) and the log spectral distance (LSD) between the reference HRTF and the estimated HRTF are measured. For subjective evaluation, the MUSHRA test and preference test are conducted. As a result, the proposed method can make listeners experience more immersive audio than the previous methods.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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• pp.1430-1436
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• 2007

A principal components analysis (PCA) of the median head-related impulse responses (HRIRs) in the CIPIC HRTF database reveals that the individual HRIRs can be adequately reconstructed by a linear combination of 12 orthonormal basis functions. These basis functions can be used generally to model arbitrary HRIRs, which are not included in the process to obtain the basis functions. To clarify whether these basis functions can be used to model other set of arbitrary HRIRs, an numerical error analysis for modeling and a series of subjective listening tests were carried out using the measured and modeled HRIRs. The results showed that the set of individual HRIRs, which were measured in our lab using different measurement conditions, techniques, and source positions, can be well modeled with reasonable accuracy. Furthermore, all subjects reported not only the accurate vertical perception but also the front-back discrimination with the modeled HRIRs based on 12 basis functions. However, as less basis functions were used for HRIR modeling, the modeling accuracy and localization performance deteriorated.

• 한국음향학회지
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• 제29권5호
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• pp.339-346
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• 2010

음향 간섭제거기는 머리전달함수 (head-related impulse response; HRIR)의 길이에 큰 영향을 받게 되어, 일반적으로 큰 차수의 필터를 필요로 한다. 간섭제거필터의 길이를 줄이기 위한 방법으로 주파수 워핑, 공통 극점과 영점 (common acoustical pole and zero; CAPZ) 모델링 등의 방법들이 제안되었는데, 본 논문에서는 이 두 가지 방법을 결합한 방법을 제안한다. 이를 위해, 주파수 워핑 영역에서 공통 극점과 영점 모델링을 통해 필터를 설계하며, 디워핑 과정을 통해 종래의 선형 영역에서 안정된 필터를 구현한다. 제안된 방법은 주파수 워핑을 통한 간섭제거 성능 향상과 공통 극점 모델링을 통한 필터 계수 감소를 함께 제공할 수 있다. 이러한 성능을 검증하기 위해 다양한 컴퓨터 모의 실험을 진행하였다.