HRTF magnitude synthesis via sparse representation of anthropometric features
Acoustics, Speech and Signal Processing(2014)
摘要
We propose a method for the synthesis of the magnitudes of Head-related Transfer Functions (HRTFs) using a sparse representation of anthropometric features. Our approach treats the HRTF synthesis problem as finding a sparse representation of the subject's anthropometric features w.r.t. the anthropometric features in the training set. The fundamental assumption is that the magnitudes of a given HRTF set can be described by the same sparse combination as the anthropometric data. Thus, we learn a sparse vector that represents the subject's anthropometric features as a linear superposition of the anthropometric features of a small subset of subjects from the training data. Then, we apply the same sparse vector directly on the HRTF tensor data. For evaluation purpose we use a new dataset, containing both anthropometric features and HRTFs. We compare the proposed sparse representation based approach with ridge regression and with the data of a manikin (which was designed based on average anthropometric data), and we simulate the best and the worst possible classifiers to select one of the HRTFs from the dataset. For instrumental evaluation we use log-spectral distortion. Experiments show that our sparse representation outperforms all other evaluated techniques, and that the synthesized HRTFs are almost as good as the best possible HRTF classifier.
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关键词
audio signal processing,data handling,pattern classification,regression analysis,speech synthesis,transfer functions,vectors,HRTF classifier,HRTF magnitude synthesis,HRTF tensor data,anthropometric data,anthropometric feature linear superposition,anthropometric feature sparse representation,head-related transfer function magnitude synthesis,log-spectral distortion,manikin data,ridge regression,sparse combination,sparse vector,Anthropometric Features,HRTF Personalization,HRTF Synthesis,Head-related Transfer Function,Sparse Representation
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