-@incollection{muller_lyrics--audio_2012,
- address = {Wadern},
- title = {Lyrics-to-{Audio} {Alignment} and its {Application}},
- isbn = {978-3-939897-37-8},
- url = {http://nbn-resolving.de/urn:nbn:de:0030-drops-27851},
- language = {English},
- urldate = {2017-03-02},
- booktitle = {Multimodal {Music} {Processing}},
- publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH},
- author = {Goto, Masataka and Fujihara, Hiromasa},
- editor = {Müller, Meinard},
- year = {2012},
- note = {OCLC: 864001691},
- pages = {23--36},
- file = {3.pdf:/home/mrl/.mozilla/firefox/7b4r727h.default-1470981082057/zotero/storage/A4ZSSMW5/3.pdf:application/pdf}
-}
-
-@inproceedings{pedone_phoneme-level_2011,
- title = {Phoneme-{Level} {Text} to {Audio} {Synchronization} on {Speech} {Signals} with {Background} {Music}.},
- url = {http://ai2-s2-pdfs.s3.amazonaws.com/7fb2/210b6a9e69ea7ca0e4f496548544781c8a8b.pdf},
- urldate = {2017-03-02},
- booktitle = {{INTERSPEECH}},
- author = {Pedone, Agnes and Burred, Juan José and Maller, Simon and Leveau, Pierre},
- year = {2011},
- pages = {433--436},
- file = {210b6a9e69ea7ca0e4f496548544781c8a8b.pdf:/home/mrl/.mozilla/firefox/7b4r727h.default-1470981082057/zotero/storage/NQR3WB2S/210b6a9e69ea7ca0e4f496548544781c8a8b.pdf:application/pdf}
-}
-
-@inproceedings{fujihara_automatic_2006,
- title = {Automatic synchronization between lyrics and music {CD} recordings based on {Viterbi} alignment of segregated vocal signals},
- url = {http://ieeexplore.ieee.org/abstract/document/4061176/},
- urldate = {2017-03-02},
- booktitle = {Multimedia, 2006. {ISM}'06. {Eighth} {IEEE} {International} {Symposium} on},
- publisher = {IEEE},
- author = {Fujihara, Hiromasa and Goto, Masataka and Ogata, Jun and Komatani, Kazunori and Ogata, Tetsuya and Okuno, Hiroshi G.},
- year = {2006},
- pages = {257--264},
- file = {04061176.pdf:/home/mrl/.mozilla/firefox/7b4r727h.default-1470981082057/zotero/storage/6DU997E4/04061176.pdf:application/pdf}
-}
-
-@inproceedings{mesaros_adaptation_2009,
- title = {Adaptation of a speech recognizer for singing voice},
- url = {http://ieeexplore.ieee.org/abstract/document/7077626/},
- urldate = {2017-03-02},
- booktitle = {Signal {Processing} {Conference}, 2009 17th {European}},
- publisher = {IEEE},
- author = {Mesaros, Annamaria and Virtanen, Tuomas},
- year = {2009},
- pages = {1779--1783},
- file = {07077626.pdf:/home/mrl/.mozilla/firefox/7b4r727h.default-1470981082057/zotero/storage/FN9TWMMJ/07077626.pdf:application/pdf}
-}
-
-@article{mesaros_automatic_2010,
- title = {Automatic {Recognition} of {Lyrics} in {Singing}},
- volume = {2010},
- issn = {1687-4714, 1687-4722},
- url = {http://asmp.eurasipjournals.com/content/2010/1/546047},
- doi = {10.1155/2010/546047},
+@article{yang_machine_2012,
+ title = {Machine {Recognition} of {Music} {Emotion}: {A} {Review}},
+ volume = {3},
+ issn = {21576904},
+ shorttitle = {Machine {Recognition} of {Music} {Emotion}},
+ url = {http://dl.acm.org/citation.cfm?doid=2168752.2168754},
+ doi = {10.1145/2168752.2168754},
language = {en},
+ number = {3},
urldate = {2017-03-02},
- journal = {EURASIP Journal on Audio, Speech, and Music Processing},
- author = {Mesaros, Annamaria and Virtanen, Tuomas},
- year = {2010},
- pages = {1--11},
- file = {art%3A10.1155%2F2010%2F546047.pdf:/home/mrl/.mozilla/firefox/7b4r727h.default-1470981082057/zotero/storage/3BR5E733/art%3A10.1155%2F2010%2F546047.pdf:application/pdf}
+ journal = {ACM Transactions on Intelligent Systems and Technology},
+ author = {Yang, Yi-Hsuan and Chen, Homer H.},
+ month = may,
+ year = {2012},
+ pages = {1--30},
+ file = {TST00040.dvi - a40-yang.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/RGP3XNGT/a40-yang.pdf:application/pdf}
}
@article{dzhambazov_automatic_2016,
urldate = {2017-03-02},
author = {Dzhambazov, Georgi and Yang, Yile and Repetto, Rafael Caro and Serra, Xavier},
year = {2016},
- file = {Automatic Alignment of Long Syllables in a Cappella Beijing Opera - viewcontent.cgi:/home/mrl/.mozilla/firefox/7b4r727h.default-1470981082057/zotero/storage/CSCH3FAK/viewcontent.pdf:application/pdf}
-}
-
-@inproceedings{mesaros_automatic_2008,
- title = {Automatic alignment of music audio and lyrics},
- url = {http://legacy.spa.aalto.fi/dafx08/papers/dafx08_57.pdf},
- urldate = {2017-03-02},
- booktitle = {Proceedings of the 11th {Int}. {Conference} on {Digital} {Audio} {Effects} ({DAFx}-08)},
- author = {Mesaros, Annamaria and Virtanen, Tuomas},
- year = {2008},
- file = {dafx08_57.pdf:/home/mrl/.mozilla/firefox/7b4r727h.default-1470981082057/zotero/storage/H24VX8KW/dafx08_57.pdf:application/pdf}
-}
-
-@inproceedings{berenzweig_locating_2001,
- title = {Locating singing voice segments within music signals},
- url = {http://ieeexplore.ieee.org/abstract/document/969557/},
- urldate = {2017-03-02},
- booktitle = {Applications of {Signal} {Processing} to {Audio} and {Acoustics}, 2001 {IEEE} {Workshop} on the},
- publisher = {IEEE},
- author = {Berenzweig, Adam L. and Ellis, Daniel PW},
- year = {2001},
- pages = {119--122},
- file = {Locating singing voice segments within music signals - Applicationis of Signal Processing to Audio and Acoustics, 2001 IEEE Workshop on the - 00969557.pdf:/home/mrl/.mozilla/firefox/7b4r727h.default-1470981082057/zotero/storage/DWBBQPDE/00969557.pdf:application/pdf}
+ file = {Automatic Alignment of Long Syllables in a Cappella Beijing Opera - viewcontent.cgi:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/CSCH3FAK/viewcontent.pdf:application/pdf}
}
@inproceedings{dzhambazov_automatic_2014,
author = {Dzhambazov, Georgi and Sentürk, Sertan and Serra, Xavier},
year = {2014},
pages = {61--64},
- file = {lyrics-to-audio-FMA_full_paper.pdf:/home/mrl/.mozilla/firefox/7b4r727h.default-1470981082057/zotero/storage/K7WFQSR8/lyrics-to-audio-FMA_full_paper.pdf:application/pdf}
+ file = {lyrics-to-audio-FMA_full_paper.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/K7WFQSR8/lyrics-to-audio-FMA_full_paper.pdf:application/pdf}
}
-@inproceedings{fujihara_three_2008,
- title = {Three techniques for improving automatic synchronization between music and lyrics: {Fricative} detection, filler model, and novel feature vectors for vocal activity detection},
- shorttitle = {Three techniques for improving automatic synchronization between music and lyrics},
- url = {http://ieeexplore.ieee.org/abstract/document/4517548/},
- urldate = {2017-03-02},
- booktitle = {Acoustics, {Speech} and {Signal} {Processing}, 2008. {ICASSP} 2008. {IEEE} {International} {Conference} on},
+@inproceedings{kato_acoustic_2013,
+ title = {Acoustic {Features} and {Auditory} {Impressions} of {Death} {Growl} and {Screaming} {Voice}},
+ isbn = {978-0-7695-5120-3},
+ url = {http://ieeexplore.ieee.org/document/6846676/},
+ doi = {10.1109/IIH-MSP.2013.120},
+ urldate = {2017-04-11},
publisher = {IEEE},
- author = {Fujihara, Hiromasa and Goto, Masataka},
- year = {2008},
- pages = {69--72},
- file = {THREE TECHNIQUES FOR IMPROVING AUTOMATIC SYNCHRONIZATION BETWEEN MUSIC AND LYRICS\: FRICATIVE DETECTION, FILLER MODEL, AND NOVEL FEATURE VECTORS FOR VOCAL ACTIVITY DETECTION - 04517548.pdf:/home/mrl/.mozilla/firefox/7b4r727h.default-1470981082057/zotero/storage/CMGJ32AM/04517548.pdf:application/pdf}
+ author = {Kato, Keizo and Ito, Akinori},
+ month = oct,
+ year = {2013},
+ pages = {460--463},
+ file = {Acoustic Features and Auditory Impressions of Death Growl and Screaming Voice - 06846676.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/VAT5AGPP/06846676.pdf:application/pdf}
}
-@article{yang_machine_2012,
- title = {Machine {Recognition} of {Music} {Emotion}: {A} {Review}},
- volume = {3},
- issn = {21576904},
- shorttitle = {Machine {Recognition} of {Music} {Emotion}},
- url = {http://dl.acm.org/citation.cfm?doid=2168752.2168754},
- doi = {10.1145/2168752.2168754},
- language = {en},
- number = {3},
- urldate = {2017-03-02},
- journal = {ACM Transactions on Intelligent Systems and Technology},
- author = {Yang, Yi-Hsuan and Chen, Homer H.},
- month = may,
- year = {2012},
- pages = {1--30},
- file = {TST00040.dvi - a40-yang.pdf:/home/mrl/.mozilla/firefox/7b4r727h.default-1470981082057/zotero/storage/RGP3XNGT/a40-yang.pdf:application/pdf}
+@article{boersma_praat_2002,
+ title = {Praat, a system for doing phonetics by computer},
+ volume = {5},
+ journal = {Glot international},
+ author = {Boersma, Paulus Petrus Gerardus},
+ year = {2002}
}
-@article{fujihara_lyricsynchronizer:_2011,
- title = {{LyricSynchronizer}: {Automatic} {Synchronization} {System} {Between} {Musical} {Audio} {Signals} and {Lyrics}},
- volume = {5},
- issn = {1932-4553, 1941-0484},
- shorttitle = {{LyricSynchronizer}},
- url = {http://ieeexplore.ieee.org/document/5876296/},
- doi = {10.1109/JSTSP.2011.2159577},
- number = {6},
- urldate = {2017-03-02},
- journal = {IEEE Journal of Selected Topics in Signal Processing},
- author = {Fujihara, Hiromasa and Goto, Masataka and Ogata, Jun and Okuno, Hiroshi G.},
- month = oct,
+@inproceedings{leglaive_singing_2015,
+ title = {Singing voice detection with deep recurrent neural networks},
+ url = {http://ieeexplore.ieee.org/abstract/document/7177944/},
+ urldate = {2017-04-25},
+ booktitle = {Acoustics, {Speech} and {Signal} {Processing} ({ICASSP}), 2015 {IEEE} {International} {Conference} on},
+ publisher = {IEEE},
+ author = {Leglaive, Simon and Hennequin, Romain and Badeau, Roland},
+ year = {2015},
+ pages = {121--125},
+ file = {SINGING VOICE DETECTION WITH DEEP RECURRENT NEURAL NETWORKS - 07177944.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/5K4JZDXC/07177944.pdf:application/pdf}
+}
+
+@book{tsatsishvili_automatic_2011,
+ title = {Automatic subgenre classification of heavy metal music},
+ url = {https://jyx.jyu.fi/dspace/handle/123456789/37227},
+ urldate = {2017-03-06},
+ author = {Tsatsishvili, Valeri},
year = {2011},
- pages = {1252--1261},
- file = {untitled - 05876296.pdf:/home/mrl/.mozilla/firefox/7b4r727h.default-1470981082057/zotero/storage/Q9MQTWHC/05876296.pdf:application/pdf}
+ file = {AUTOMATIC-SUBGENRE-CLASSIFICATION-OF-HEAVY-METAL-MUSIC.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/3HTFFPVN/AUTOMATIC-SUBGENRE-CLASSIFICATION-OF-HEAVY-METAL-MUSIC.pdf:application/pdf}
}
@article{mauch_integrating_2012,
month = jan,
year = {2012},
pages = {200--210},
- file = {untitled - 05876304.pdf:/home/mrl/.mozilla/firefox/7b4r727h.default-1470981082057/zotero/storage/MM4NI9SJ/05876304.pdf:application/pdf}
+ file = {untitled - 05876304.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/MM4NI9SJ/05876304.pdf:application/pdf}
}
-@book{tsatsishvili_automatic_2011,
- title = {Automatic subgenre classification of heavy metal music},
- url = {https://jyx.jyu.fi/dspace/handle/123456789/37227},
- urldate = {2017-03-06},
- author = {Tsatsishvili, Valeri},
- year = {2011},
- file = {AUTOMATIC-SUBGENRE-CLASSIFICATION-OF-HEAVY-METAL-MUSIC.pdf:/home/mrl/.mozilla/firefox/7b4r727h.default-1470981082057/zotero/storage/3HTFFPVN/AUTOMATIC-SUBGENRE-CLASSIFICATION-OF-HEAVY-METAL-MUSIC.pdf:application/pdf}
+@inproceedings{saunders_real-time_1996,
+ title = {Real-time discrimination of broadcast speech/music},
+ volume = {2},
+ booktitle = {Acoustics, {Speech}, and {Signal} {Processing}, 1996. {ICASSP}-96. {Conference} {Proceedings}., 1996 {IEEE} {International} {Conference} on},
+ publisher = {IEEE},
+ author = {Saunders, John},
+ year = {1996},
+ pages = {993--996},
+ file = {REAL-TIME DISCRIMINATION OF BROADCAST SPEECH/MUSIC - Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE Inte - saunders_j2.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/H9PG94BN/saunders_j2.pdf:application/pdf}
}
-@inproceedings{sturm_survey_2012,
- title = {A survey of evaluation in music genre recognition},
- booktitle = {International {Workshop} on {Adaptive} {Multimedia} {Retrieval}},
- publisher = {Springer},
- author = {Sturm, Bob L},
- year = {2012},
- pages = {29--66},
- file = {Sturm20121212.pdf:/home/mrl/.mozilla/firefox/7b4r727h.default-1470981082057/zotero/storage/6MJKTRHE/Sturm20121212.pdf:application/pdf}
+@inproceedings{nwe_singing_2004,
+ title = {Singing voice detection in popular music},
+ url = {http://dl.acm.org/citation.cfm?id=1027602},
+ urldate = {2017-04-25},
+ booktitle = {Proceedings of the 12th annual {ACM} international conference on {Multimedia}},
+ publisher = {ACM},
+ author = {Nwe, Tin Lay and Shenoy, Arun and Wang, Ye},
+ year = {2004},
+ pages = {324--327},
+ file = {p324-nwe.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/HD48B4K8/p324-nwe.pdf:application/pdf}
}
@article{you_comparative_2015,
author = {You, Shingchern D. and Wu, Yi-Chung and Peng, Shih-Hsien},
month = aug,
year = {2015},
- file = {11042_2015_2894_Article 1..16 - you2015.pdf:/home/mrl/.mozilla/firefox/7b4r727h.default-1470981082057/zotero/storage/QQIS2H44/you2015.pdf:application/pdf}
+ file = {11042_2015_2894_Article 1..16 - you2015.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/QQIS2H44/you2015.pdf:application/pdf}
}
-@article{boersma_praat_2002,
- title = {Praat, a system for doing phonetics by computer},
- volume = {5},
- journal = {Glot international},
- author = {Boersma, Paulus Petrus Gerardus and {others}},
- year = {2002}
+@inproceedings{fujihara_automatic_2006,
+ title = {Automatic synchronization between lyrics and music {CD} recordings based on {Viterbi} alignment of segregated vocal signals},
+ url = {http://ieeexplore.ieee.org/abstract/document/4061176/},
+ urldate = {2017-03-02},
+ booktitle = {Multimedia, 2006. {ISM}'06. {Eighth} {IEEE} {International} {Symposium} on},
+ publisher = {IEEE},
+ author = {Fujihara, Hiromasa and Goto, Masataka and Ogata, Jun and Komatani, Kazunori and Ogata, Tetsuya and Okuno, Hiroshi G.},
+ year = {2006},
+ pages = {257--264},
+ file = {04061176.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/6DU997E4/04061176.pdf:application/pdf}
}
-@inproceedings{saunders_real-time_1996,
- title = {Real-time discrimination of broadcast speech/music},
- volume = {2},
- booktitle = {Acoustics, {Speech}, and {Signal} {Processing}, 1996. {ICASSP}-96. {Conference} {Proceedings}., 1996 {IEEE} {International} {Conference} on},
- publisher = {IEEE},
- author = {Saunders, John},
- year = {1996},
- pages = {993--996},
- file = {REAL-TIME DISCRIMINATION OF BROADCAST SPEECH/MUSIC - Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE Inte - saunders_j2.pdf:/home/mrl/.mozilla/firefox/7b4r727h.default-1470981082057/zotero/storage/H9PG94BN/saunders_j2.pdf:application/pdf}
+@incollection{muller_lyrics--audio_2012,
+ address = {Wadern},
+ title = {Lyrics-to-{Audio} {Alignment} and its {Application}},
+ isbn = {978-3-939897-37-8},
+ url = {http://nbn-resolving.de/urn:nbn:de:0030-drops-27851},
+ language = {English},
+ urldate = {2017-03-02},
+ booktitle = {Multimodal {Music} {Processing}},
+ publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH},
+ author = {Goto, Masataka and Fujihara, Hiromasa},
+ editor = {Müller, Meinard},
+ year = {2012},
+ note = {OCLC: 864001691},
+ pages = {23--36},
+ file = {3.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/A4ZSSMW5/3.pdf:application/pdf}
}
@inproceedings{scheirer_construction_1997,
pages = {1331--1334}
}
-@inproceedings{kato_acoustic_2013,
- title = {Acoustic {Features} and {Auditory} {Impressions} of {Death} {Growl} and {Screaming} {Voice}},
- isbn = {978-0-7695-5120-3},
- url = {http://ieeexplore.ieee.org/document/6846676/},
- doi = {10.1109/IIH-MSP.2013.120},
- urldate = {2017-04-11},
+@inproceedings{mesaros_adaptation_2009,
+ title = {Adaptation of a speech recognizer for singing voice},
+ url = {http://ieeexplore.ieee.org/abstract/document/7077626/},
+ urldate = {2017-03-02},
+ booktitle = {Signal {Processing} {Conference}, 2009 17th {European}},
publisher = {IEEE},
- author = {Kato, Keizo and Ito, Akinori},
- month = oct,
- year = {2013},
- pages = {460--463},
- file = {Acoustic Features and Auditory Impressions of Death Growl and Screaming Voice - 06846676.pdf:/home/mrl/.mozilla/firefox/7b4r727h.default-1470981082057/zotero/storage/VAT5AGPP/06846676.pdf:application/pdf}
+ author = {Mesaros, Annamaria and Virtanen, Tuomas},
+ year = {2009},
+ pages = {1779--1783},
+ file = {07077626.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/FN9TWMMJ/07077626.pdf:application/pdf}
+}
+
+@inproceedings{fujihara_three_2008,
+ title = {Three techniques for improving automatic synchronization between music and lyrics: {Fricative} detection, filler model, and novel feature vectors for vocal activity detection},
+ shorttitle = {Three techniques for improving automatic synchronization between music and lyrics},
+ url = {http://ieeexplore.ieee.org/abstract/document/4517548/},
+ urldate = {2017-03-02},
+ booktitle = {Acoustics, {Speech} and {Signal} {Processing}, 2008. {ICASSP} 2008. {IEEE} {International} {Conference} on},
+ publisher = {IEEE},
+ author = {Fujihara, Hiromasa and Goto, Masataka},
+ year = {2008},
+ pages = {69--72},
+ file = {THREE TECHNIQUES FOR IMPROVING AUTOMATIC SYNCHRONIZATION BETWEEN MUSIC AND LYRICS\: FRICATIVE DETECTION, FILLER MODEL, AND NOVEL FEATURE VECTORS FOR VOCAL ACTIVITY DETECTION - 04517548.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/CMGJ32AM/04517548.pdf:application/pdf}
+}
+
+@inproceedings{berenzweig_locating_2001,
+ title = {Locating singing voice segments within music signals},
+ url = {http://ieeexplore.ieee.org/abstract/document/969557/},
+ urldate = {2017-03-02},
+ booktitle = {Applications of {Signal} {Processing} to {Audio} and {Acoustics}, 2001 {IEEE} {Workshop} on the},
+ publisher = {IEEE},
+ author = {Berenzweig, Adam L. and Ellis, Daniel PW},
+ year = {2001},
+ pages = {119--122},
+ file = {Locating singing voice segments within music signals - Applicationis of Signal Processing to Audio and Acoustics, 2001 IEEE Workshop on the - 00969557.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/DWBBQPDE/00969557.pdf:application/pdf}
+}
+
+@misc{friis_vikings_2004,
+ title = {Vikings and their {Music}},
+ url = {http://www.viking.no/e/life/music/e-musikk-mogens.html},
+ urldate = {2017-04-11},
+ author = {Friis, Mogens},
+ year = {2004},
+ file = {Vikings and their Music:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/SEEXI3VR/e-musikk-mogens.html:text/html}
}
@inproceedings{sakakibara_growl_2004,
booktitle = {Proc. {Int}. {Symp}. on {Musical} {Acoustics}},
author = {Sakakibara, K. and Fuks, Leonardo and Imagawa, Hiroshi and Tayama, Niro and Naganuma, D.},
year = {2004},
- file = {isma04.pdf:/home/mrl/.mozilla/firefox/7b4r727h.default-1470981082057/zotero/storage/PUFH652B/isma04.pdf:application/pdf}
+ file = {isma04.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/PUFH652B/isma04.pdf:application/pdf}
}
-@misc{friis_vikings_2004,
- title = {Vikings and their {Music}},
- url = {http://www.viking.no/e/life/music/e-musikk-mogens.html},
- urldate = {2017-04-11},
- author = {Friis, Mogens},
- year = {2004},
- file = {Vikings and their Music:/home/mrl/.mozilla/firefox/7b4r727h.default-1470981082057/zotero/storage/SEEXI3VR/e-musikk-mogens.html:text/html}
-}
\ No newline at end of file
+@inproceedings{vembu_separation_2005,
+ title = {Separation of {Vocals} from {Polyphonic} {Audio} {Recordings}.},
+ url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.139.5510&rep=rep1&type=pdf},
+ urldate = {2017-04-25},
+ booktitle = {{ISMIR}},
+ publisher = {Citeseer},
+ author = {Vembu, Shankar and Baumann, Stephan},
+ year = {2005},
+ pages = {337--344},
+ file = {ismir05.dvi - download:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/WZ7INPRU/download.pdf:application/pdf}
+}
+
+@inproceedings{pedone_phoneme-level_2011,
+ title = {Phoneme-{Level} {Text} to {Audio} {Synchronization} on {Speech} {Signals} with {Background} {Music}.},
+ url = {http://ai2-s2-pdfs.s3.amazonaws.com/7fb2/210b6a9e69ea7ca0e4f496548544781c8a8b.pdf},
+ urldate = {2017-03-02},
+ booktitle = {{INTERSPEECH}},
+ author = {Pedone, Agnes and Burred, Juan José and Maller, Simon and Leveau, Pierre},
+ year = {2011},
+ pages = {433--436},
+ file = {210b6a9e69ea7ca0e4f496548544781c8a8b.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/NQR3WB2S/210b6a9e69ea7ca0e4f496548544781c8a8b.pdf:application/pdf}
+}
+
+@inproceedings{mesaros_automatic_2008,
+ title = {Automatic alignment of music audio and lyrics},
+ url = {http://legacy.spa.aalto.fi/dafx08/papers/dafx08_57.pdf},
+ urldate = {2017-03-02},
+ booktitle = {Proceedings of the 11th {Int}. {Conference} on {Digital} {Audio} {Effects} ({DAFx}-08)},
+ author = {Mesaros, Annamaria and Virtanen, Tuomas},
+ year = {2008},
+ file = {dafx08_57.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/H24VX8KW/dafx08_57.pdf:application/pdf}
+}
+
+@article{mesaros_automatic_2010,
+ title = {Automatic {Recognition} of {Lyrics} in {Singing}},
+ volume = {2010},
+ issn = {1687-4714, 1687-4722},
+ url = {http://asmp.eurasipjournals.com/content/2010/1/546047},
+ doi = {10.1155/2010/546047},
+ language = {en},
+ urldate = {2017-03-02},
+ journal = {EURASIP Journal on Audio, Speech, and Music Processing},
+ author = {Mesaros, Annamaria and Virtanen, Tuomas},
+ year = {2010},
+ pages = {1--11},
+ file = {art%3A10.1155%2F2010%2F546047.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/3BR5E733/art%3A10.1155%2F2010%2F546047.pdf:application/pdf}
+}
+
+@article{fujihara_lyricsynchronizer:_2011,
+ title = {{LyricSynchronizer}: {Automatic} {Synchronization} {System} {Between} {Musical} {Audio} {Signals} and {Lyrics}},
+ volume = {5},
+ issn = {1932-4553, 1941-0484},
+ shorttitle = {{LyricSynchronizer}},
+ url = {http://ieeexplore.ieee.org/document/5876296/},
+ doi = {10.1109/JSTSP.2011.2159577},
+ number = {6},
+ urldate = {2017-03-02},
+ journal = {IEEE Journal of Selected Topics in Signal Processing},
+ author = {Fujihara, Hiromasa and Goto, Masataka and Ogata, Jun and Okuno, Hiroshi G.},
+ month = oct,
+ year = {2011},
+ pages = {1252--1261},
+ file = {untitled - 05876296.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/Q9MQTWHC/05876296.pdf:application/pdf}
+}
+
+@inproceedings{sturm_survey_2012,
+ title = {A survey of evaluation in music genre recognition},
+ booktitle = {International {Workshop} on {Adaptive} {Multimedia} {Retrieval}},
+ publisher = {Springer},
+ author = {Sturm, Bob L},
+ year = {2012},
+ pages = {29--66},
+ file = {Sturm20121212.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/6MJKTRHE/Sturm20121212.pdf:application/pdf}
+}
+
+@inproceedings{williams_speech/music_1999,
+ title = {Speech/music discrimination based on posterior probability features.},
+ volume = {99},
+ url = {https://pdfs.semanticscholar.org/1662/dba5ab1fc87e871605d1fc14b89b0b1a029c.pdf},
+ urldate = {2017-05-16},
+ booktitle = {Eurospeech},
+ author = {Williams, Gethin and Ellis, Daniel PW},
+ year = {1999},
+ pages = {687--690},
+ file = {euro99-uttclass.dvi - dba5ab1fc87e871605d1fc14b89b0b1a029c.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/PZDIDK4Q/dba5ab1fc87e871605d1fc14b89b0b1a029c.pdf:application/pdf}
+}
+
+@inproceedings{berenzweig_using_2002,
+ title = {Using voice segments to improve artist classification of music},
+ url = {http://www.aes.org/e-lib/browse.cfm?elib=11147},
+ urldate = {2017-05-16},
+ booktitle = {Audio {Engineering} {Society} {Conference}: 22nd {International} {Conference}: {Virtual}, {Synthetic}, and {Entertainment} {Audio}},
+ publisher = {Audio Engineering Society},
+ author = {Berenzweig, Adam L. and Ellis, Daniel PW and Lawrence, Steve},
+ year = {2002},
+ file = {aes02-aclass.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/WJHA7NW6/aes02-aclass.pdf:application/pdf}
+}
+
+@inproceedings{rocamora_comparing_2007,
+ title = {Comparing audio descriptors for singing voice detection in music audio files},
+ volume = {26},
+ url = {https://pdfs.semanticscholar.org/b1c0/d8188b6459a47993c814f212556e02fcfc91.pdf},
+ urldate = {2017-05-16},
+ booktitle = {Brazilian symposium on computer music, 11th. san pablo, brazil},
+ author = {Rocamora, Martın and Herrera, Perfecto},
+ year = {2007},
+ pages = {27},
+ file = {sbcm2007Singing.dvi - d8188b6459a47993c814f212556e02fcfc91.pdf:/home/mrl/.mozilla/firefox/a614qfce.default/zotero/storage/3SMMC6VR/d8188b6459a47993c814f212556e02fcfc91.pdf:application/pdf}
+}
%&asr
-\usepackage[nonumberlist,acronyms]{glossaries}
-%\makeglossaries%
+\usepackage[toc,nonumberlist,acronyms]{glossaries}
+\makeglossaries%
\newacronym{ANN}{ANN}{Artificial Neural Network}
\newacronym{HMM}{HMM}{Hidden Markov Model}
\newacronym{GMM}{GMM}{Gaussian Mixture Models}
\newacronym{FA}{FA}{Forced alignment}
\newacronym{MFC}{MFC}{Mel-frequency cepstrum}
\newacronym{MFCC}{MFCC}{\acrlong{MFC} coefficient}
+\newacronym{PPF}{PPF}{Posterior Probability Features}
+\newacronym{MLP}{MLP}{Multi-layer Perceptron}
+\newacronym{PLP}{PLP}{Perceptual Linear Prediction}
+\newacronym{ZCR}{ZCR}{Zero-crossing Rate}
+\newacronym{LPC}{LPC}{Linear Prediction Coefficients}
+\newacronym{LPCC}{LPCC}{\acrlong{LPC} derivec cepstrum}
\newacronym{IFPI}{IFPI}{International Federation of the Phonographic Industry}
\newglossaryentry{dm}{name={Death Metal},
description={is an extreme heavy metal music style with growling vocals and
frequency representation}}
\newglossaryentry{MS}{name={Mel-Scale},
description={is a human ear inspired scale for spectral signals.}}
+\newglossaryentry{Viterbi}{name={Viterbi},
+ description={is a dynamic programming algorithm for finding the most likely
+ sequence of hidden states in a \gls{HMM}}}
\begin{document}
\frontmatter{}
\tableofcontents
-%Glossaries
-%\glsaddall{}
-%\printglossaries
-
\mainmatter{}
%Berenzweig and Ellis use acoustic classifiers from speech recognition as a
%detector for singing lines. They achive 80\% accuracy for forty 15 second
%Introduction, leading to a clearly defined research question
\chapter{Introduction}
-\section{Introduction}
-The primary medium for music distribution is rapidly changing from physical
-media to digital media. The \gls{IFPI} stated that about $43\%$ of music
-revenue rises from digital distribution. Another $39\%$ arises from the
-physical sale and the remaining $16\%$ is made through performance and
-synchronisation revenieus. The overtake of digital formats on physical formats
-took place somewhere in 2015. Moreover, ever since twenty years the music
-industry has seen significant growth
-again\footnote{\url{http://www.ifpi.org/facts-and-stats.php}}.
-
-There has always been an interest in lyrics to music alignment to be used in
-for example karaoke. As early as in the late 1980s karaoke machines were
-available for consumers. While the lyrics for the track are almost always
-available, a alignment is not and it involves manual labour to create such an
-alignment.
-
-A lot of this musical distribution goes via non-official channels such as
-YouTube\footnote{\url{https://youtube.com}} in which fans of the performers
-often accompany the music with synchronized lyrics. This means that there is an
-enormous treasure of lyrics-annotated music available but not within our reach
-since the subtitles are almost always hardcoded into the video stream and thus
-not directly usable as data. Because of this interest it is very useful to
-device automatic techniques for segmenting instrumental and vocal parts of a
-song, apply forced alignment or even lyrics recognition on the audio file.
-
-Such techniques are heavily researched and working systems have been created.
-However, these techniques are designed to detect a clean singing voice and have
-not been testen on so-called \emph{extended vocal techniques} such as grunting
-or growling. Growling is heavily used in extreme metal genres such as \gls{dm}
-but it must be noted that grunting is not a technique only used in extreme
-metal styles. Similar or equal techniques have been used in \emph{Beijing
-opera}, Japanese \emph{Noh} and but also more western styles like jazz singing
-by Louis Armstrong\cite{sakakibara_growl_2004}. It might even be traced back
-to viking times. For example, an arab merchant visiting a village in Denmark
-wrote in the tenth century\cite{friis_vikings_2004}:
-
-\begin{displayquote}
- Never before I have heard uglier songs than those of the Vikings in
- Slesvig. The growling sound coming from their throats reminds me of dogs
- howling, only more untamed.
-\end{displayquote}
-
-\section{\gls{dm}}
-
-%Literature overview / related work
-\section{Related work}
-The field of applying standard speech processing techniques on music started in
-the late 90s\cite{saunders_real-time_1996,scheirer_construction_1997} and it
-was found that music has different discriminating features compared to normal
-speech.
-
-Berenzweig and Ellis expanded on the aforementioned research by trying to
-separate singing from instrumental music\cite{berenzweig_locating_2001}.
-
-\todo{Incorporate this in literary framing}%
-~\cite{fujihara_automatic_2006}%
-~\cite{fujihara_lyricsynchronizer:_2011}%
-~\cite{fujihara_three_2008}%
-~\cite{mauch_integrating_2012}%
-~\cite{mesaros_adaptation_2009}%
-~\cite{mesaros_automatic_2008}%
-~\cite{mesaros_automatic_2010}%
-~%\cite{muller_multimodal_2012}%
-~\cite{pedone_phoneme-level_2011}%
-~\cite{yang_machine_2012}%
-
-
-
-\section{Research question}
-It is discutable whether the aforementioned techniques work because the
-spectral properties of a growling voice is different from the spectral
-properties of a clean singing voice. It has been found that growling voices
-have less prominent peaks in the frequency representation and are closer to
-noise then clean singing\cite{kato_acoustic_2013}. This leads us to the
-research question:
-
-\begin{center}\em%
- Are standard \gls{ANN} based techniques for singing voice detection
- suitable for non-standard musical genres like \gls{dm}.
-\end{center}
+\input{intro.tex}
\chapter{Methods}
-%Methodology
-
-%Experiment(s) (set-up, data, results, discussion)
-\section{Data \& Preprocessing}
-To run the experiments data has been collected from several \gls{dm} albums.
-The exact data used is available in Appendix~\ref{app:data}. The albums are
-extracted from the audio CD and converted to a mono channel waveform with the
-correct samplerate \emph{SoX}\footnote{\url{http://sox.sourceforge.net/}}.
-Every file is annotated using
-Praat\cite{boersma_praat_2002} where the utterances are manually aligned to
-the audio. Examples of utterances are shown in
-Figure~\ref{fig:bloodstained} and Figure~\ref{fig:abominations} where the
-waveform, $1-8000$Hz spectrals and annotations are shown. It is clearly visible
-that within the genre of death metal there are a different spectral patterns
-visible.
-
-\begin{figure}[ht]
- \centering
- \includegraphics[width=.7\linewidth]{cement}
- \caption{A vocal segment of the \emph{Cannibal Corpse} song
- \emph{Bloodstained Cement}}\label{fig:bloodstained}
-\end{figure}
-
-\begin{figure}[ht]
- \centering
- \includegraphics[width=.7\linewidth]{abominations}
- \caption{A vocal segment of the \emph{Disgorge} song
- \emph{Enthroned Abominations}}\label{fig:abominations}
-\end{figure}
-
-The data is collected from three studio albums. The
-first band is called \emph{Cannibal Corpse} and has been producing \gls{dm} for
-almost 25 years and have been creating the same type every album. The singer of
-\emph{Cannibal Corpse} has a very raspy growls and the lyrics are quite
-comprehensible. The vocals produced by \emph{Cannibal Corpse} are bordering
-regular shouting.
-
-The second band is called \emph{Disgorge} and make even more violently sounding
-music. The growls of the lead singer sound like a coffee grinder and are more
-shallow. In the spectrals it is clearly visible that there are overtones
-produced during some parts of the growling. The lyrics are completely
-incomprehensible and therefore some parts were not annotated with the actual
-lyrics because it was not possible what was being sung.
-
-Lastly a band from Moscow is chosen bearing the name \emph{Who Dies in
-Siberian Slush}. This band is a little odd compared to the previous \gls{dm}
-bands because they create \gls{dom}. \gls{dom} is characterized by the very
-slow tempo and low tuned guitars. The vocalist has a very characteristic growl
-and performs in several moscovian bands. This band also stands out because it
-uses piano's and synthesizers. The droning synthesizers often operate in the
-same frequency as the vocals.
-
-\section{\gls{MFCC} Features}
-The waveforms in itself are not very suitable to be used as features due to the
-high dimensionality and correlation. Therefore we use the aften used
-\glspl{MFCC} feature vectors.\todo{cite which papers use this} The actual
-conversion is done using the \emph{python\_speech\_features}%
-\footnote{\url{https://github.com/jameslyons/python_speech_features}} package.
-
-\gls{MFCC} features are nature inspired and built incrementally in a several of
-steps.
-\begin{enumerate}
- \item The first step in the process is converting the time representation
- of the signal to a spectral representation using a sliding window with
- overlap. The width of the window and the step size are two important
- parameters in the system. In classical phonetic analysis window sizes
- of $25ms$ with a step of $10ms$ are often chosen because they are small
- enough to only contain subphone entities. Singing for $25ms$ is
- impossible so it is arguable that the window size is very small.
- \item The standard \gls{FT} gives a spectral representation that has
- linearly scaled frequencies. This scale is converted to the \gls{MS}
- using triangular overlapping windows.
- \item
-\end{enumerate}
-
-
-\todo{Explain why MFCC and which parameters}
-
-\section{\gls{ANN} Classifier}
-\todo{Spectrals might be enough, no decorrelation}
-
-\section{Model training}
-
-\section{Experiments}
-
-\section{Results}
-
+\input{methods.tex}
\chapter{Conclusion \& Discussion}
-\section{Conclusion}
-This research shows that existing techniques for singing-voice detection
-designed for regular singing voices also work respectably on extreme singing
-styles like grunting. With a standard \gls{ANN} classifier using \gls{MFCC}
-features a performance of $85\%$ can be achieved. When applying smoothing this
-can be increased until\todo{results}.
+\input{conclusion.tex}
-%Discussion section
-\section{Discussion}
-Singing-voice detection can be seen as a crude way of
-genre-discrimination.\todo{finish}
-
-\todo{Novelty}
-\todo{Weaknesses}
-\todo{Dataset is not very varied but\ldots}
-
-\todo{Doom metal}
-%Conclusion section
-%Acknowledgements
-%Statement on authors' contributions
%(Appendices)
\appendix
-\chapter{Experimental data}\label{app:data}
-\begin{table}[h]
- \centering
- \begin{tabular}{cllll}
- \toprule
- Num. & Artist & Album & Song & Duration\\
- \midrule
- 00 & Cannibal Corpse & A Skeletal Domain & High Velocity Impact Spatter & 04:06.91\\
- 01 & Cannibal Corpse & A Skeletal Domain & Sadistic Embodiment & 03:17.31\\
- 02 & Cannibal Corpse & A Skeletal Domain & Kill or Become & 03:50.67\\
- 03 & Cannibal Corpse & A Skeletal Domain & A Skeletal Domain & 03:38.77\\
- 04 & Cannibal Corpse & A Skeletal Domain & Headlong Into Carnage & 03:01.25\\
- 05 & Cannibal Corpse & A Skeletal Domain & The Murderer's Pact & 05:05.23\\
- 06 & Cannibal Corpse & A Skeletal Domain & Funeral Cremation & 03:41.89\\
- 07 & Cannibal Corpse & A Skeletal Domain & Icepick Lobotomy & 03:16.24\\
- 08 & Cannibal Corpse & A Skeletal Domain & Vector of Cruelty & 03:25.15\\
- 09 & Cannibal Corpse & A Skeletal Domain & Bloodstained Cement & 03:41.99\\
- 10 & Cannibal Corpse & A Skeletal Domain & Asphyxiate to Resuscitate & 03:47.40\\
- 11 & Cannibal Corpse & A Skeletal Domain & Hollowed Bodies & 03:05.80\\
- 12 & Disgorge & Parallels of Infinite Torture & Revealed in Obscurity & 05:13.20\\
- 13 & Disgorge & Parallels of Infinite Torture & Enthroned Abominations & 04:05.39\\
- 14 & Disgorge & Parallels of Infinite Torture & Atonement & 02:57.36\\
- 15 & Disgorge & Parallels of Infinite Torture & Abhorrent Desecration of Thee Iniquity & 04:17.20\\
- 16 & Disgorge & Parallels of Infinite Torture & Forgotten Scriptures & 02:01.72\\
- 17 & Disgorge & Parallels of Infinite Torture & Descending Upon Convulsive Devourment & 04:38.85\\
- 18 & Disgorge & Parallels of Infinite Torture & Condemned to Sufferance & 04:57.59\\
- 19 & Disgorge & Parallels of Infinite Torture & Parallels of Infinite Torture & 05:03.33\\
- 20 & Disgorge & Parallels of Infinite Torture & Asphyxiation of Thee Oppressed & 05:42.37\\
- 21 & Disgorge & Parallels of Infinite Torture & Ominous Sigils of Ungodly Ruin & 04:59.15\\
- 22 & Who Dies In Siberian Slush & Bitterness Of The Years That Are Lost & Leave Me & 06:35.60\\
- 23 & Who Dies In Siberian Slush & Bitterness Of The Years That Are Lost & The Woman We Are Looking For & 06:53.63\\
- 24 & Who Dies In Siberian Slush & Bitterness Of The Years That Are Lost & M\"obius Ring & 07:20.56\\
- 25 & Who Dies In Siberian Slush & Bitterness Of The Years That Are Lost & Interlude & 04:26.49\\
- 26 & Who Dies In Siberian Slush & Bitterness Of The Years That Are Lost & Завещание Гумилёва & 08:46.76\\
- 27 & Who Dies In Siberian Slush & Bitterness Of The Years That Are Lost & An Old Road Through The Snow & 02:31.56\\
- 28 & Who Dies In Siberian Slush & Bitterness Of The Years That Are Lost & Bitterness Of The Years That Are Lost & 09:10.49\\
- \midrule
- & & & Total: & 02:13:40\\
- \bottomrule
- \end{tabular}
- \caption{Songs used in the experiments}
-\end{table}
+\input{appendices.tex}
+
+\newpage
+%Glossaries
+\glsaddall{}
+\begingroup
+\let\clearpage\relax
+\let\cleardoublepage\relax
+\printglossaries{}
+\endgroup
\bibliographystyle{ieeetr}
\bibliography{asr}
--- /dev/null
+\section{Introduction}
+The primary medium for music distribution is rapidly changing from physical
+media to digital media. The \gls{IFPI} stated that about $43\%$ of music
+revenue rises from digital distribution. Another $39\%$ arises from the
+physical sale and the remaining $16\%$ is made through performance and
+synchronisation revenieus. The overtake of digital formats on physical formats
+took place somewhere in 2015. Moreover, ever since twenty years the music
+industry has seen significant growth
+again\footnote{\url{http://www.ifpi.org/facts-and-stats.php}}.
+
+There has always been an interest in lyrics to music alignment to be used in
+for example karaoke. As early as in the late 1980s karaoke machines were
+available for consumers. While the lyrics for the track are almost always
+available, a alignment is not and it involves manual labour to create such an
+alignment.
+
+A lot of this musical distribution goes via non-official channels such as
+YouTube\footnote{\url{https://youtube.com}} in which fans of the performers
+often accompany the music with synchronized lyrics. This means that there is an
+enormous treasure of lyrics-annotated music available but not within our reach
+since the subtitles are almost always hardcoded into the video stream and thus
+not directly usable as data. Because of this interest it is very useful to
+device automatic techniques for segmenting instrumental and vocal parts of a
+song, apply forced alignment or even lyrics recognition on the audio file.
+
+Such techniques are heavily researched and working systems have been created.
+However, these techniques are designed to detect a clean singing voice and have
+not been testen on so-called \emph{extended vocal techniques} such as grunting
+or growling. Growling is heavily used in extreme metal genres such as \gls{dm}
+but it must be noted that grunting is not a technique only used in extreme
+metal styles. Similar or equal techniques have been used in \emph{Beijing
+opera}, Japanese \emph{Noh} and but also more western styles like jazz singing
+by Louis Armstrong\cite{sakakibara_growl_2004}. It might even be traced back
+to viking times. For example, an arab merchant visiting a village in Denmark
+wrote in the tenth century\cite{friis_vikings_2004}:
+
+\begin{displayquote}
+ Never before I have heard uglier songs than those of the Vikings in
+ Slesvig. The growling sound coming from their throats reminds me of dogs
+ howling, only more untamed.
+\end{displayquote}
+
+\section{\gls{dm}}
+
+%Literature overview / related work
+\section{Related work}
+Applying speech related processing and classification techniques on music
+already started in the late 90s. Saunders et al.\ devised a technique to
+classify audio in the categories \emph{Music} and \emph{Speech}. It was found
+that music has different properties than speech. Music has more bandwidth,
+tonality and regularity. Multivariate Gaussian classifiers were used to
+discriminate the categories with an average performance of $90\%$.
+
+Williams and Ellis were inspired by the aforementioned research and tried to
+separate the singing segments from the instrumental
+segments\cite{williams_speech/music_1999}. This was later verified by
+Berenzweig and Ellis\cite{berenzweig_locating_2001}. The latter became the de
+facto literature on singing voice detection. Both show that features derived
+from \gls{PPF} such as energy and distribution are highly effective in
+separating speech from non-speech signals such as music. The data used was
+already segmented.
+
+Later, Berenzweig showed singing voice segments to be more useful for artist
+classification and used a \gls{MLP} using \gls{PLP} coefficients to separate
+detect singing voice\cite{berenzweig_using_2002}. Nwe et al.\ showed that there
+is not much difference in accuracy when using different features founded in
+speech processing. They tested several features and found accuracies differ
+less that a few percent. Moreover, they found that others have tried to tackle
+the problem using myriads of different approaches such as using \gls{ZCR},
+\gls{MFCC} and \gls{LPCC} as features and \glspl{HMM} or \glspl{GMM} as
+classifiers\cite{nwe_singing_2004}.
+
+Fujihara et al.\ took the idea to a next level by attempting to do \gls{FA} on
+music. Their approach is a three step approach. First step is reducing the
+accompaniment levels, secondly the vocal segments are
+separated from the non-vocal segments using a simple two-state \gls{HMM}.
+The chain is concluded by applying \gls{Viterbi} alignment on the segregated
+signals with the lyrics. The system showed accuracy levels of $90\%$ on
+Japanese music\cite{fujihara_automatic_2006}. Later they improved
+hereupon\cite{fujihara_three_2008} and even made a ready to use karaoke
+application that can do the this online\cite{fujihara_lyricsynchronizer:_2011}.
+
+Singing voice detection can also be seen as a binary genre recognition problem.
+Therefore the techniques used in that field might be of use. Genre recognition
+has a long history that can be found in the survey by
+Sturm\cite{sturm_survey_2012}. It must be noted that of all the $485$ papers
+cited by Sturm only one master thesis is applying genre recognition on heavy
+metal genres\cite{tsatsishvili_automatic_2011}.
+
+Singing voice detection has been tried on less conventional styles in the past.
+Dzhambazov et al.\ proposed to align long syllables in Beijing Opera to the
+audio\cite{dzhambazov_automatic_2016}. Beijing Opera sometimes contains
+growling like vocals. Dzhambazov also tried aligning lyrics to audio in
+classical Turkish music\cite{dzhambazov_automatic_2014}.
+
+\section{Research question}
+It is discutable whether the aforementioned techniques work because the
+spectral properties of a growling voice is different from the spectral
+properties of a clean singing voice. It has been found that growling voices
+have less prominent peaks in the frequency representation and are closer to
+noise then clean singing\cite{kato_acoustic_2013}. This leads us to the
+research question:
+
+\begin{center}\em%
+ Are standard \gls{ANN} based techniques for singing voice detection
+ suitable for non-standard musical genres like \gls{dm} and \gls{dom}.
+\end{center}
repositories / asr1617.git / commitdiff