Head-Related Transfer Function Generator Using Pinna Shape Data
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Solution Overview
Problem
Conventional head-related transfer function selecting devices can only select from pre-stored functions and require accurate measurement in anechoic chambers, which are limited and difficult for general users to access, especially those without acoustic knowledge.
Innovation Solution
A head-related transfer function generator that acquires and models head-related impulse responses using a window function, Fourier transforms, and discriminant analysis to derive frequency bands and relative amplitudes, allowing for the generation of a modeled head-related transfer function without direct measurement, and integrates pinna shape data to identify relevant frequency bands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If head-related transfer function is measured in an anechoic chamber, then measurement precision is improved, but device complexity and accessibility are worsened
Solution Approach 1:
The patent creates a virtual model (copy) of the head-related transfer function based on pinna shape data, replacing the need for physical measurement in complex anechoic chambers. The system generates a modeled head-related transfer function that replicates the acoustic characteristics without requiring specialized measurement environments.
Solution Approach 2:
The patent replaces the mechanical/acoustic measurement system (requiring anechoic chambers and specialized equipment) with a computational system that uses machine learning models and algorithms to generate head-related transfer functions from pinna shape data.
2Ease of operation
If head-related transfer function is measured in ordinary environments, then ease of operation is improved, but measurement precision is worsened
Solution Approach 1:
The system creates a computational copy of the acoustic measurement process, allowing measurements to be performed in ordinary environments while maintaining precision through algorithmic processing and machine learning models that compensate for environmental variations.
3Productivity
If database of head-related transfer functions is used, then productivity is improved, but adaptability is worsened
Solution Approach 1:
The system performs preliminary processing of pinna shape data and pre-trains machine learning models with diverse head-related transfer function data, enabling rapid generation of customized results without requiring full measurements or database searches for each listener.
Solution Approach 2:
The system changes the input parameters from requiring actual acoustic measurements to using only pinna shape geometric data, allowing the same input data to serve multiple purposes and enabling both rapid processing and high adaptability through the flexible machine learning framework.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the reproduction of head-related transfer functions for listeners without actual measurement, improving accessibility and accuracy in three-dimensional acoustic systems and virtual reality applications.
Implementation Method 1
an early head-related transfer function generating unit configured to calculate an initial head-related impulse response by applying a window function to the actually measured head-related impulse response and generate data representing an early head-related transfer function by performing a Fourier transform on the initial head-related impulse response
Data Source
AI summary
An object is to acquire a head-related transfer function reproducing features of a head-related transfer function of a listener without actually measuring the head-related transfer function of the listener. A head-related transfer function generator includes: acquiring data that represents an actually measured head-related impulse response of sound waves arriving at external auditory meatus entrances of a listener for training; calculating an initial head-related impulse response by applying a window function to the actually measured head-related impulse response and generating data representing an early head-related transfer function by performing a Fourier transform on the initial head-related impulse response; dividing the early head-related transfer function into a plurality of frequency bands; and executing a process of extracting a peak or a notch on the basis of curvature of the early head-related transfer function and a process of determining a relative amplitude for each of the plurality of frequency bands and generating data representing a modeled head-related transfer function of the listener for training by interpolating points representing the relative amplitudes.