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A Personalized Head-Related Transfer Function Modeling Method Based on Deep Neural Network

A deep neural network, head-related transmission technology, applied in the field of head-related transfer function, personalized head-related transfer function modeling, can solve the problem of not being able to predict HRTF and so on

Active Publication Date: 2021-11-09
PEKING UNIV
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Problems solved by technology

However, this method requires a separate HRTF mapping for each spatial direction, and cannot predict the HRTF of the unsampled spatial directions in the HRTF database.

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  • A Personalized Head-Related Transfer Function Modeling Method Based on Deep Neural Network
  • A Personalized Head-Related Transfer Function Modeling Method Based on Deep Neural Network
  • A Personalized Head-Related Transfer Function Modeling Method Based on Deep Neural Network

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Embodiment Construction

[0044] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The preferred embodiments of the present invention will be described in more detail below with reference to the accompanying drawings of the present invention.

[0045] The specific implementation steps of the method of the invention include data preprocessing, spatial principal component analysis and neural network modeling. The present invention adopts CIPIC database, which contains HRIR data of D=1250 directions, and the sampling rate is 44.1kHz. The specific implementation process of each step is as follows:

[0046] 1. Data preprocessing

[0047] To preprocess the original HRIR data in the CIPIC library, the specific steps are as follows:

[0048] First, transform the HRIR data into the frequency domain. Perform Fourier transform on the HRIR in the CIPIC library to obtain the HRTF in the frequency domain. The HRTF in the frequency domain is the horizontal angle θ, the elevation angle A function of the frequenc...

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Abstract

The invention discloses a method for modeling a personalized head-related transfer function based on a deep neural network. This method decomposes the HRTF data based on the spatial principal component analysis, and uses the neural network to model the spatial principal components, spatial principal component coefficients, and average spatial functions obtained from the decomposition. The spatial principal components and average spatial functions are only related to the spatial direction. , the spatial principal component coefficient is a function of the frequency and the individual characteristic parameters of the subject; the present invention uses a deep neural network to model the spatial principal component, the average spatial function and the binaural time difference respectively, and introduces spatial direction information such as horizontal angle and elevation angle into the network The input layer; meanwhile, a neural network is used to model the spatial principal component coefficients based on anthropometric parameters. Based on the above model, according to a small number of anthropometric parameters of the subjects, their personalized HRTF in any direction in space can be obtained.

Description

technical field [0001] The invention belongs to the technical field of signal processing, relates to a head-related transfer function, in particular to a modeling method of a personalized head-related transfer function based on spatial principal component analysis. Background technique [0002] Hearing plays a very important role in human life. It senses the sound of the surrounding environment to make corresponding judgments and decisions. In addition to subjective attributes such as the intensity, pitch, and timbre of sound, human hearing can also make judgments about the direction and distance of the sound source. After the sound wave from the sound source reaches the human ears, it is finally perceived by the scattering and reflection of the listener's head, auricle, torso and other human body structures. The study of human's ability to perceive the spatial characteristics of sound has always been a research topic in acoustics and auditory psychology, and has very impo...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06N3/04G06F2218/12G06F18/2135G06F18/2411
Inventor 曲天书吴玺宏张梦帆
Owner PEKING UNIV
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