A Sparse Spherical Harmonic-Based Model in Subbands for Head-Related Transfer Functions

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	A Sparse Spherical Harmonic-Based Model in Subbands for Head-Related Transfer Functions
	Xiaoke Qi1 ; Jianhua Tao1,2
	2016
会议日期	September 8–12, 2016
会议地点	San Francisco, USA
英文摘要	Several functional models for head-related transfer function (HRTF) have been proposed based on spherical harmonic (SH) orthogonal functions, which yield an encouraging performance level in terms of log-spectral distortion (LSD). However, since the properties of subbands are quite different and highly subject-dependent, the degree of SH expansion should be adapted to the subband and the subject, which is quite challenging. In this paper, a sparse spherical harmonic-based model termed SSHM is proposed in order to achieve an intelligent frequency truncation. Different from SH-based model (SHM) which assigns the degree for each subband, SSHM constrains the number of SH coefficients by using an l_1 penalty, and automatically preserves the significant coefficients in each subband. As a result, SSHM requires less coefficients at the same SD level than other truncation methods to reconstruct HRTFs. Furthermore, when used for interpolation, SSHM gives a better fitting precision since it naturally reduces the influence of the fluctuation caused by the movement of the subject and the processing error. The experiments show that even using about 40% less coefficients, SSHM has a slightly lower LSD than SHM. Therefore, SSHM can achieve a better tradeoff between efficiency and accuracy.
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/15469]
专题	自动化研究所_模式识别国家重点实验室_人机语音交互团队
作者单位	1.National Laboratory of Pattern Recognition (NLPR) 2.CAS Center for Excellence in Brain Science and Intelligence Technology
推荐引用方式 GB/T 7714	Xiaoke Qi,Jianhua Tao. A Sparse Spherical Harmonic-Based Model in Subbands for Head-Related Transfer Functions[C]. 见:. San Francisco, USA. September 8–12, 2016.