S. Barrass, "Digital Fabrication of Acoustic Sonifications," J. Audio Eng. Soc., vol. 60, no. 9, pp. 709-715, (2012 September.). doi:
S. Barrass, "Digital Fabrication of Acoustic Sonifications," J. Audio Eng. Soc., vol. 60 Issue 9 pp. 709-715, (2012 September.). doi:
Abstract: Because the human brain is often optimal for detecting subtle patterns, this paper explores a novel transformation that maps numerical data into sound. In this research, a set of data taken from head-related transfer functions was used to create physical objects (bells made from stainless steel) whose acoustics were then presented to listeners. The technique is called acoustic sonification. Listeners were able to hear differences in pitch and timbre of bells that were constructed from different datasets, while bells constructed from similar datasets sounded similar. Modulating the shape of a bell with a dataset can influence the acoustic spectrum in a way that results in audible differences |even though there was no apparent visual difference. Acoustic sonification can take advantage of auditory pattern recognition.
@article{barrass2012digital,
author={barrass, stephen},
journal={journal of the audio engineering society},
title={digital fabrication of acoustic sonifications},
year={2012},
volume={60},
number={9},
pages={709-715},
doi={},
month={september},}
@article{barrass2012digital,
author={barrass, stephen},
journal={journal of the audio engineering society},
title={digital fabrication of acoustic sonifications},
year={2012},
volume={60},
number={9},
pages={709-715},
doi={},
month={september},
abstract={because the human brain is often optimal for detecting subtle patterns, this paper explores a novel transformation that maps numerical data into sound. in this research, a set of data taken from head-related transfer functions was used to create physical objects (bells made from stainless steel) whose acoustics were then presented to listeners. the technique is called acoustic sonification. listeners were able to hear differences in pitch and timbre of bells that were constructed from different datasets, while bells constructed from similar datasets sounded similar. modulating the shape of a bell with a dataset can influence the acoustic spectrum in a way that results in audible differences |even though there was no apparent visual difference. acoustic sonification can take advantage of auditory pattern recognition.},}
TY - paper
TI - Digital Fabrication of Acoustic Sonifications
SP - 709
EP - 715
AU - Barrass, Stephen
PY - 2012
JO - Journal of the Audio Engineering Society
IS - 9
VO - 60
VL - 60
Y1 - September 2012
TY - paper
TI - Digital Fabrication of Acoustic Sonifications
SP - 709
EP - 715
AU - Barrass, Stephen
PY - 2012
JO - Journal of the Audio Engineering Society
IS - 9
VO - 60
VL - 60
Y1 - September 2012
AB - Because the human brain is often optimal for detecting subtle patterns, this paper explores a novel transformation that maps numerical data into sound. In this research, a set of data taken from head-related transfer functions was used to create physical objects (bells made from stainless steel) whose acoustics were then presented to listeners. The technique is called acoustic sonification. Listeners were able to hear differences in pitch and timbre of bells that were constructed from different datasets, while bells constructed from similar datasets sounded similar. Modulating the shape of a bell with a dataset can influence the acoustic spectrum in a way that results in audible differences |even though there was no apparent visual difference. Acoustic sonification can take advantage of auditory pattern recognition.
Because the human brain is often optimal for detecting subtle patterns, this paper explores a novel transformation that maps numerical data into sound. In this research, a set of data taken from head-related transfer functions was used to create physical objects (bells made from stainless steel) whose acoustics were then presented to listeners. The technique is called acoustic sonification. Listeners were able to hear differences in pitch and timbre of bells that were constructed from different datasets, while bells constructed from similar datasets sounded similar. Modulating the shape of a bell with a dataset can influence the acoustic spectrum in a way that results in audible differences |even though there was no apparent visual difference. Acoustic sonification can take advantage of auditory pattern recognition.
