Own voice detection with near field head related transfer function based on frequency domain binaural model

2012 ◽  
Vol 131 (4) ◽  
pp. 3350-3350
Author(s):  
Taira Onoguchi ◽  
Yoshifumi Chisaki ◽  
Tsuyoshi Usagawa
Keyword(s):  
Transfer Function ◽  
Frequency Domain ◽  
Near Field ◽  
Head Related Transfer Function ◽  
Voice Detection

scholarly journals Measurement of Head-Related Transfer Functions: A Review

2020 ◽  
Vol 10 (14) ◽  
pp. 5014 ◽  
Author(s):  
Song Li ◽  
Jürgen Peissig
Keyword(s):  
Transfer Function ◽  
Transfer Functions ◽  
Human Subjects ◽  
Near Field ◽  
Free Field ◽  
Measurement Systems ◽  
High Measurement ◽  
Head Related Transfer Function ◽  
Acoustic Environments ◽  
Acoustic Transfer Function

A head-related transfer function (HRTF) describes an acoustic transfer function between a point sound source in the free-field and a defined position in the listener’s ear canal, and plays an essential role in creating immersive virtual acoustic environments (VAEs) reproduced over headphones or loudspeakers. HRTFs are highly individual, and depend on directions and distances (near-field HRTFs). However, the measurement of high-density HRTF datasets is usually time-consuming, especially for human subjects. Over the years, various novel measurement setups and methods have been proposed for the fast acquisition of individual HRTFs while maintaining high measurement accuracy. This review paper provides an overview of various HRTF measurement systems and some insights into trends in individual HRTF measurements.

Error analyses of a Multi-Input-Multi-Output cantilever beam test

2021 ◽  
pp. 107754632110337
Author(s):  
Arup Maji ◽  
Fernando Moreu ◽  
James Woodall ◽  
Maimuna Hossain
Keyword(s):  
Transfer Function ◽  
Frequency Domain ◽  
Cantilever Beam ◽  
Transfer Functions ◽  
Linear Superposition ◽  
Transfer Function Matrix ◽  
Error Analyses ◽  
Pseudo Inverse ◽  
Function Matrix

Multi-Input-Multi-Output vibration testing typically requires the determination of inputs to achieve desired response at multiple locations. First, the responses due to each input are quantified in terms of complex transfer functions in the frequency domain. In this study, two Inputs and five Responses were used leading to a 5 × 2 transfer function matrix. Inputs corresponding to the desired Responses are then computed by inversion of the rectangular matrix using Pseudo-Inverse techniques that involve least-squared solutions. It is important to understand and quantify the various sources of errors in this process toward improved implementation of Multi-Input-Multi-Output testing. In this article, tests on a cantilever beam with two actuators (input controlled smart shakers) were used as Inputs while acceleration Responses were measured at five locations including the two input locations. Variation among tests was quantified including its impact on transfer functions across the relevant frequency domain. Accuracy of linear superposition of the influence of two actuators was quantified to investigate the influence of relative phase information. Finally, the accuracy of the Multi-Input-Multi-Output inversion process was investigated while varying the number of Responses from 2 (square transfer function matrix) to 5 (full-rectangular transfer function matrix). Results were examined in the context of the resonances and anti-resonances of the system as well as the ability of the actuators to provide actuation energy across the domain. Improved understanding of the sources of uncertainty from this study can be used for more complex Multi-Input-Multi-Output experiments.

scholarly journals Spatial Unmasking Effect on Speech Reception Threshold in the Median Plane

2020 ◽  
Vol 10 (15) ◽  
pp. 5257
Author(s):  
Nathan Berwick ◽  
Hyunkook Lee
Keyword(s):  
Transfer Function ◽  
Input Signal ◽  
Function Analysis ◽  
Median Plane ◽  
Speech Reception ◽  
Transfer Function Analysis ◽  
Spatial Unmasking ◽  
Head Related Transfer Function ◽  
Speech Reception Thresholds ◽  
Energy Weighting

This study examined whether the spatial unmasking effect operates on speech reception thresholds (SRTs) in the median plane. SRTs were measured using an adaptive staircase procedure, with target speech sentences and speech-shaped noise maskers presented via loudspeakers at −30°, 0°, 30°, 60° and 90°. Results indicated a significant median plane spatial unmasking effect, with the largest SRT gain obtained for the −30° elevation of the masker. Head-related transfer function analysis suggests that the result is associated with the energy weighting of the ear-input signal of the masker at upper-mid frequencies relative to the maskee.

Sign in / Sign up

Export Citation Format

Share Document