J. Shier, K. McNally, and G. Tzanetakis, "Analysis of Drum Machine Kick and Snare Sounds," Paper 9887, (2017 October.). doi:
J. Shier, K. McNally, and G. Tzanetakis, "Analysis of Drum Machine Kick and Snare Sounds," Paper 9887, (2017 October.). doi:
Abstract: The use of electronic drum samples is widespread in contemporary music productions, with music producers having an unprecedented number of samples available to them. The development of new tools to assist users organizing and managing libraries of this type requires comprehensive audio analysis that is distinct from that used for general classification or onset detection tasks. In this paper 4230 kick and snare samples, representing 250 individual electronic drum machines are evaluated. Samples are segmented into different lengths and analyzed using comprehensive audio feature analysis. Audio classification is used to evaluate and compare the effect of this time segmentation and establish the overall effectiveness of the selected feature set. Results demonstrate that there is improvement in classification scores when using time segmentation as a pre-processing step.
@article{shier2017analysis,
author={shier, jordie and mcnally, kirk and tzanetakis, george},
journal={journal of the audio engineering society},
title={analysis of drum machine kick and snare sounds},
year={2017},
volume={},
number={},
pages={},
doi={},
month={october},}
@article{shier2017analysis,
author={shier, jordie and mcnally, kirk and tzanetakis, george},
journal={journal of the audio engineering society},
title={analysis of drum machine kick and snare sounds},
year={2017},
volume={},
number={},
pages={},
doi={},
month={october},
abstract={the use of electronic drum samples is widespread in contemporary music productions, with music producers having an unprecedented number of samples available to them. the development of new tools to assist users organizing and managing libraries of this type requires comprehensive audio analysis that is distinct from that used for general classification or onset detection tasks. in this paper 4230 kick and snare samples, representing 250 individual electronic drum machines are evaluated. samples are segmented into different lengths and analyzed using comprehensive audio feature analysis. audio classification is used to evaluate and compare the effect of this time segmentation and establish the overall effectiveness of the selected feature set. results demonstrate that there is improvement in classification scores when using time segmentation as a pre-processing step.},}
TY - paper
TI - Analysis of Drum Machine Kick and Snare Sounds
SP -
EP -
AU - Shier, Jordie
AU - McNally, Kirk
AU - Tzanetakis, George
PY - 2017
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2017
TY - paper
TI - Analysis of Drum Machine Kick and Snare Sounds
SP -
EP -
AU - Shier, Jordie
AU - McNally, Kirk
AU - Tzanetakis, George
PY - 2017
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2017
AB - The use of electronic drum samples is widespread in contemporary music productions, with music producers having an unprecedented number of samples available to them. The development of new tools to assist users organizing and managing libraries of this type requires comprehensive audio analysis that is distinct from that used for general classification or onset detection tasks. In this paper 4230 kick and snare samples, representing 250 individual electronic drum machines are evaluated. Samples are segmented into different lengths and analyzed using comprehensive audio feature analysis. Audio classification is used to evaluate and compare the effect of this time segmentation and establish the overall effectiveness of the selected feature set. Results demonstrate that there is improvement in classification scores when using time segmentation as a pre-processing step.
The use of electronic drum samples is widespread in contemporary music productions, with music producers having an unprecedented number of samples available to them. The development of new tools to assist users organizing and managing libraries of this type requires comprehensive audio analysis that is distinct from that used for general classification or onset detection tasks. In this paper 4230 kick and snare samples, representing 250 individual electronic drum machines are evaluated. Samples are segmented into different lengths and analyzed using comprehensive audio feature analysis. Audio classification is used to evaluate and compare the effect of this time segmentation and establish the overall effectiveness of the selected feature set. Results demonstrate that there is improvement in classification scores when using time segmentation as a pre-processing step.
Open Access
Authors:
Shier, Jordie; McNally, Kirk; Tzanetakis, George
Affiliations:
University of Victoria, Victoria, Canada; University of Victoria, School of Music, Victoria, BC, Canada; University of Victoria, Victoria, BC, Canada(See document for exact affiliation information.)
AES Convention:
143 (October 2017)
Paper Number:
9887
Publication Date:
October 8, 2017Import into BibTeX
Subject:
Applications in Audio
Permalink:
http://www.aes.org/e-lib/browse.cfm?elib=19284