Sound Localization: Human Vs. Machine

被引:0
|
作者
Jayaweera, W. G. Nuwan [1 ]
Buddhika, A. G. [1 ]
Jayasekara, P. [1 ]
Abeykoon, A. M. Harsha S. [1 ]
机构
[1] Univ Moratuwa, Dept Elect Engn, Moratuwa, Sri Lanka
关键词
ear sensation; error quantification; Head Related Transfer Function (HRTF); Inter-aural Level Difference (ILD); Inter-aural Time Difference (ITD);
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The Human shows a remarkable capability in localizing a sound source and navigating towards it. In the current context of robotic applications, machinery models have been developed, so that they can be used in sound source localization. But, it is not yet quantified the accuracy of human's sound source localization in different frequencies and distances at the free field. Thus, the aim of this paper is to estimate the error of human's sound source localization at different frequencies and distances on the horizontal plane and the paper presents the characteristics of ear by taking each individual's localization ability into consideration. An experiment is conducted to investigate the individual ability to predict the sound incident direction. Ten samples of asian young adults from the age group of 20 - 30 years are taken into the experiment and their responses for localization cues are recorded. The experiment is also conducted for different sound source locations such as 1m, 2m and 3m and different sound source frequencies of 1 kHz and 5 kHz. The results show the individual responses for the direction prediction and they are unique from each individual to the other. The average percentage errors for direction prediction at 1 kHz frequency sound signal give 0.20, 0.93 and 5.20 for 1m, 2m and 3m distances respectively. Also, the average percentage errors for direction prediction at 5 kHz frequency sound signal give 3.59, 1.68 and 0.52 for 1m, 2m and 3m distances respectively.
引用
收藏
页数:6
相关论文
共 50 条
  • [1] Human vs. Machine - or with Machine?
    Giannakopoulos, Triantafillos G.
    Kyriazanos, Ioannis
    [J]. EUROPEAN JOURNAL OF VASCULAR AND ENDOVASCULAR SURGERY, 2021, 62 (06) : 878 - 878
  • [2] Intermediality and Human vs. Machine Translation
    Huang, Harry J.
    [J]. CLCWEB-COMPARATIVE LITERATURE AND CULTURE, 2011, 13 (03):
  • [3] Measurement of vitiligo: human vs. machine
    Edwards, C.
    [J]. BRITISH JOURNAL OF DERMATOLOGY, 2019, 180 (05) : 991 - 991
  • [4] ChatGPT and exercise prescription: Human vs. machine or human plus machine?
    Cavazzotto, Timothy Gustavo
    Dantas, Diego Bessa
    Queiroga, Marcos Roberto
    [J]. JOURNAL OF SPORT AND HEALTH SCIENCE, 2024, 13 (05) : 661 - 662
  • [5] Multimodal Fusion Strategies: Human vs. Machine
    Ko, Hanseok
    [J]. AVSU'18: PROCEEDINGS OF THE 2018 WORKSHOP ON AUDIO-VISUAL SCENE UNDERSTANDING FOR IMMERSIVE MULTIMEDIA, 2018, : 1 - 1
  • [6] Human vs. machine: evaluation of fluorescence micrographs
    Nattkemper, TW
    Twellmann, T
    Ritter, H
    Schubert, W
    [J]. COMPUTERS IN BIOLOGY AND MEDICINE, 2003, 33 (01) : 31 - 43
  • [7] Performance vs. competence in human-machine comparisons
    Firestone, Chaz
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2020, 117 (43) : 26562 - 26571
  • [8] A Human vs. Machine Challenge in Fashion Color Classification
    Grana, Costantino
    Borghesani, Daniele
    Cucchiara, Rita
    [J]. COMPUTER VISION - ECCV 2012, PT III, 2012, 7585 : 631 - 634
  • [9] Machine vs. Human Translation of SNOMED CT Terms
    Schulz, Stefan
    Bernhardt-Melischnig, Johannes
    Kreuzthaler, Markus
    Daumke, Philipp
    Boeker, Martin
    [J]. MEDINFO 2013: PROCEEDINGS OF THE 14TH WORLD CONGRESS ON MEDICAL AND HEALTH INFORMATICS, PTS 1 AND 2, 2013, 192 : 581 - 584
  • [10] Laconic Image Classification: Human vs. Machine Performance
    Carrasco, Javier
    Hogan, Aidan
    Perez, Jorge
    [J]. CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, 2020, : 115 - 124