elnikkis / bibtexcleaner Goto Github PK
View Code? Open in Web Editor NEWBibTex cleaner.
Home Page: https://bibtexcleaner.herokuapp.com/
BibTex cleaner.
Home Page: https://bibtexcleaner.herokuapp.com/
author+yearでIDがうまく生成されない
@article{some2000b,
author = {Sandioo Aaaaa and Aiueo, koko and Andaaa, bbbad},
Journal = {Journal of aaaaa},
number = {34},
volume = {1},
title = {A title},
year = {1010}
}
存在しないエントリタイプを入力すると落ちる
@roceedings{adachi2013,
title={{圧縮類似度による楽譜からの作曲者判定}},
author={足達花絵 and 岡部正幸 and 梅村恭司},
booktitle={DEIM Forum 2013 D1-1},
year={2013}
}
これを入れるとタイトルが3重になる
@inproceedings{adachi2013,
title={{圧縮類似度による楽譜からの作曲者判定}},
author={足達花絵 and 岡部正幸 and 梅村恭司},
booktitle={DEIM Forum 2013 D1-1},
year={2013}
}
落ちる例
@INPROCEEDINGS{Takamoto2016-rv,
title = "Improving compression based dissimilarity measure for music
score analysis",
booktitle = "2016 International Conference On Advanced Informatics: Concepts,
Theory And Application ({ICAICTA})",
author = "Takamoto, Ayaka and Umemura, Mayu and Yoshida, Mitsuo and
Umemura, Kyoji",
abstract = "In this paper, we propose a way to improve the compression based
dissimilarity measure, CDM. We propose to use a modified value
of the file size, where the original CDM uses an unmodified file
size. Our application is a music score analysis. We have chosen
piano pieces from five different composers. We have selected 75
famous pieces (15 pieces for each composer). We computed the
distances among all pieces by using the modified CDM. We use the
K-nearest neighbor method when we estimate the composer of each
piece of music. The modified CDM shows improved accuracy. The
difference is statistically significant.",
pages = "1--5",
month = aug,
year = 2016,
keywords = "Technological innovation;Estimation;Multiple signal
classification;Electronic mail;Dictionaries;Random
sequences;Computer science;Music Analysis;Motif
Discovery;Similarity Measure;Compression",
doi = "10.1109/ICAICTA.2016.7803107"
}
前回利用したときの設定値が保存されるようにする。
出力されるエントリリストがキーの辞書順にソート済み、というのをオプションで選べるようにする。
変換結果がページ遷移なしで表示されるようにする。
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