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A. Mehrabi, C. Harte, C. Baume, and S. Dixon, "Music Thumbnailing for Radio Podcasts: A Listener Evaluation," J. Audio Eng. Soc., vol. 65, no. 6, pp. 474-481, (2017 June.). doi: https://doi.org/10.17743/jaes.2017.0011 A. Mehrabi, C. Harte, C. Baume, and S. Dixon, "Music Thumbnailing for Radio Podcasts: A Listener Evaluation," J. Audio Eng. Soc., vol. 65 Issue 6 pp. 474-481, (2017 June.). doi: https://doi.org/10.17743/jaes.2017.0011
Abstract: When radio podcasts are produced from previously broadcast material, thumbnails of songs that were featured in the original program are often included. Such thumbnails provide a summary of the music content. Because creating thumbnails is a labor-intensive process, this is an ideal application for automatic music editing, but it raises the question of how a piece of music can be best summarized. Researchers asked 120 listeners to rate the quality of thumbnails generated by eight methods (five automatic and three manual). The listeners were asked to rate the editing methods based on the song part selection and transition quality in the edited clips, as well as the perceived overall quality. The listener ratings showed a preference for editing methods where the edit points were quantized to bar positions, but there was no preference for whether the chorus was included or not. Ratings for two automatic editing methods were not significantly different from their manual counterparts. This suggests that automatic editing methods can be used to create production-quality thumbnails.

@article{mehrabi2017music,
author={mehrabi, adib and harte, chris and baume, chris and dixon, simon},
journal={journal of the audio engineering society},
title={music thumbnailing for radio podcasts: a listener evaluation},
year={2017},
volume={65},
number={6},
pages={474-481},
doi={https://doi.org/10.17743/jaes.2017.0011},
month={june},} @article{mehrabi2017music,
author={mehrabi, adib and harte, chris and baume, chris and dixon, simon},
journal={journal of the audio engineering society},
title={music thumbnailing for radio podcasts: a listener evaluation},
year={2017},
volume={65},
number={6},
pages={474-481},
doi={https://doi.org/10.17743/jaes.2017.0011},
month={june},
abstract={when radio podcasts are produced from previously broadcast material, thumbnails of songs that were featured in the original program are often included. such thumbnails provide a summary of the music content. because creating thumbnails is a labor-intensive process, this is an ideal application for automatic music editing, but it raises the question of how a piece of music can be best summarized. researchers asked 120 listeners to rate the quality of thumbnails generated by eight methods (five automatic and three manual). the listeners were asked to rate the editing methods based on the song part selection and transition quality in the edited clips, as well as the perceived overall quality. the listener ratings showed a preference for editing methods where the edit points were quantized to bar positions, but there was no preference for whether the chorus was included or not. ratings for two automatic editing methods were not significantly different from their manual counterparts. this suggests that automatic editing methods can be used to create production-quality thumbnails.},}

TY - paper
TI - Music Thumbnailing for Radio Podcasts: A Listener Evaluation
SP - 474 EP - 481
AU - Mehrabi, Adib
AU - Harte, Chris
AU - Baume, Chris
AU - Dixon, Simon
PY - 2017
JO - Journal of the Audio Engineering Society
IS - 6
VO - 65
VL - 65
Y1 - June 2017 TY - paper
TI - Music Thumbnailing for Radio Podcasts: A Listener Evaluation
SP - 474 EP - 481
AU - Mehrabi, Adib
AU - Harte, Chris
AU - Baume, Chris
AU - Dixon, Simon
PY - 2017
JO - Journal of the Audio Engineering Society
IS - 6
VO - 65
VL - 65
Y1 - June 2017
AB - When radio podcasts are produced from previously broadcast material, thumbnails of songs that were featured in the original program are often included. Such thumbnails provide a summary of the music content. Because creating thumbnails is a labor-intensive process, this is an ideal application for automatic music editing, but it raises the question of how a piece of music can be best summarized. Researchers asked 120 listeners to rate the quality of thumbnails generated by eight methods (five automatic and three manual). The listeners were asked to rate the editing methods based on the song part selection and transition quality in the edited clips, as well as the perceived overall quality. The listener ratings showed a preference for editing methods where the edit points were quantized to bar positions, but there was no preference for whether the chorus was included or not. Ratings for two automatic editing methods were not significantly different from their manual counterparts. This suggests that automatic editing methods can be used to create production-quality thumbnails.