AudioCommons code for generating list of publications

example_output.txt

JOURNALS AND BOOK CHAPTERS
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* 2018
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Choi, K., Fazekas, G., Sandler, M., Cho, K. (2018). The Effects of Noisy Labels on Deep Convolutional Neural Networks for Music Tagging. In: IEEE Transactions on Emerging Topics in Computational Intelligence Vol. 2, No. 2. URL: https://ieeexplore.ieee.org/document/8323324

Liang, B., Fazekas, G., Sandler, M. (2018). Measurement, Recognition and Visualisation of Piano Pedalling Gestures and Techniques. In: Journal of the AES, Vol. 66, Issue 2. URL: http://www.aes.org/e-lib/browse.cfm?elib=19584

Xambó, A., Lerch, A., Freeman, J. (2018). Music Information Retrieval in Live Coding: A Theoretical Framework. In: Computer Music Journal.

* 2019
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Estefanía Cano, Derry FitzGerald, Antoine Liutkus, Mark D. Plumbley and Fabian-Robert Stöter (2019). Musical Source Separation: An Introduction. In: IEEE Signal Processing Magazine . URL: http://epubs.surrey.ac.uk/849940/



CONFERENCE PAPERS
*****************

* 2015
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Font, F., Serra, X. (2015). The Audio Commons Initiative. In: Proc. of the International Society for Music Information Retrieval Conference (ISMIR, late-breaking demo). URL: https://www.audiocommons.org/assets/files/audiocommons_ismir_2015.pdf

* 2016
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Allik, A., Fazekas, G., Sandler, M. (2016). An Ontology for Audio Features. In: Proc. of the International Society for Music Information Retrieval Conference (ISMIR). URL: https://wp.nyu.edu/ismir2016/wp-content/uploads/sites/2294/2016/07/077_Paper.pdf

Allik, A., Fazekas, G., Sandler, M. (2016). Ontological Representation of Audio Features. In: Proc. of the 15th International Semantic Web Conference (ISWC). URL: https://link.springer.com/chapter/10.1007/978-3-319-46547-0_1

Bogdanov, D., Porter, A., Herrera, P., Serra, X. (2016). Cross-collection evaluation for music classification tasks. In: Proc. of the International Society for Music Information Retrieval Conference (ISMIR). URL: http://mtg.upf.edu/node/3498

Buccoli, M., Zanoni, M., Fazekas, G., Sarti A., Sandler, M. (2016). A Higher-Dimensional Expansion of Affective Norms for English Terms for Music Tagging. In: Proc. of the International Society for Music Information Retrieval Conference (ISMIR). URL: https://wp.nyu.edu/ismir2016/wp-content/uploads/sites/2294/2016/07/253_Paper.pdf

Choi, K., Fazekas, G., Sandler, M. (2016). Automatic Tagging Using Deep Convolutional Neural Networks. In: Proc. of the International Society for Music Information Retrieval Conference (ISMIR). URL: https://arxiv.org/abs/1606.00298

Choi, K., Fazekas, G., Sandler, M. (2016). Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions. In: Proc. of the User Modeling, Adaptation and Personalization Conference (UMAP), Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems (SOAP). URL: https://arxiv.org/abs/1606.0209

Font, F., Brookes, T., Fazekas, G., Guerber, M., La Burthe, A., Plans, A., Plumbley, M. D., Shaashua, M., Wang, W., Serra, X. (2016). Audio Commons: bringing Creative Commons audio content to the creative industries. In: Proc. of the 61st AES Conference on Audio for Games. URL: https://www.audiocommons.org/assets/files/audiocommons_aes_2016.pdf

Font, F., Serra, X. (2016). Tempo Estimation for Music Loops and a Simple Confidence Measure. In: Proc. of the International Society for Music Information Retrieval Conference (ISMIR). URL: http://mtg.upf.edu/node/3479

Juric D., Fazekas, G. (2016). Knowledge Extraction from Audio Content Service Providers’ API Descriptions. In: Proc. of the 10th International Conference on Metadata and Semantics Research (MTSR). URL: http://link.springer.com/10.1007/978-3-319-49157-8_

Porter, A., Bogdanov, D., Serra, X. (2016). Mining metadata from the web for AcousticBrainz. In: Proc. of the 3rd International Digital Libraries for Musicology workshop. URL: http://mtg.upf.edu/node/3533

Wilmering, T., Fazekas, G., Sandler, M. (2016). AUFX-O: Novel Methods for the Representation of Audio Processing Workflows. In: Proc. of the 15th International Semantic Web Conference (ISWC). URL: https://link.springer.com/chapter/10.1007/978-3-319-46547-0_24

* 2017
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Bogdanov D., Serra X. (2017). Quantifying music trends and facts using editorial metadata from the Discogs database. In: Proc. of the International Society for Music Information Retrieval Conference (ISMIR). URL: http://hdl.handle.net/10230/32931

Bogdanov, D., Porter A., Urbano J., Schreiber H. (2017). The MediaEval 2017 AcousticBrainz Genre Task: Content-based Music Genre Recognition from Multiple Sources. In: MediaEval Workshop. URL: http://hdl.handle.net/10230/32932

Choi, K., Fazekas, G., Sandler, M., Cho, K. (2017). Convolutional Recurrent Neural Networks for Music Classification. In: Proc. of the 42nd IEEE International Conference on Acoustics. URL: https://arxiv.org/abs/1609.0424

Choi, K., Fazekas, G., Sandler, M., Cho, K. (2017). Transfer Learning for Music Classification and Regression Tasks. In: Proc. of the International Society for Music Information Retrieval Conference (ISMIR). URL: https://arxiv.org/abs/1703.09179

Fonseca, E., Gong R., Bogdanov D., Slizovskaia O., Gomez E., Serra, X. (2017). Acoustic Scene Classification by Ensembling Gradient Boosting Machine and Convolutional Neural Networks. In: Proc. of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE). URL: https://repositori.upf.edu/handle/10230/33454

Fonseca, E., Pons J., Favory X., Font F., Bogdanov D., Ferraro A., Oramas S., Porter A., Serra X. (2017). Freesound Datasets: A Platform for the Creation of Open Audio Datasets. In: Proc. of the International Society for Music Information Retrieval Conference (ISMIR). URL: http://hdl.handle.net/10230/33299

Font, F., Bandiera G. (2017). Freesound Explorer: Make Music While Discovering Freesound!. In: Proc. of the Web Audio Conference (WAC). URL: http://hdl.handle.net/10230/32538

Page, K., Bechhofer, S., Fazekas, G., Weigl, D., Wilmering, T. (2017). Realising a Layered Digital Library: Exploration and Analysis of the Live Music Archive through Linked Data. In: Proc. of the ACM/IEEE Joint Conference on Digital Libraries (JCDL). URL: http://ieeexplore.ieee.org/document/7991563

Pauwels, J., Fazekas, G., Sandler, M. (2017). Exploring Confidence Measures and Their Application in Music Labelling Systems Based on Hidden Markov Models. In: Proc. of the International Society for Music Information Retrieval Conference (ISMIR). URL: https://ismir2017.smcnus.org/wp-content/uploads/2017/10/195_Paper.pdf

Pearce, A., Brookes, T., Mason, R. (2017). Timbral attributes for sound effect library searching. In: Proc. of the Audio Engineering Society Conference on Semantic Audio. URL: http://www.aes.org/e-lib/download.cfm/18754.pdf?ID=18754

Wilmering, T., Thalmann, F., Fazekas, G., Sandler, M. (2017). Bridging Fan Communities and Facilitating Access to Music Archives Through Semantic Audio Applications. In: Proc. of the 143st Convention of the Audio Engineering Society. URL: http://eecs.qmul.ac.uk/~gyorgyf/files/papers/wilmering2017aes.pdf

* 2018
------

Bogdanov, D., Porter A., Urbano J., Schreiber H. (2018). The MediaEval 2018 AcousticBrainz Genre Task: Content-based Music Genre Recognition from Multiple Sources. In: MediaEval Workshop. URL: http://hdl.handle.net/10230/35744

Ceriani, M., Fazekas, G. (2018). Audio Commons Ontology: A Data Model for an Audio Content Ecosystem. In: Proc. of the 17th International Semantic Web Conference (ISWC). URL: https://link.springer.com/chapter/10.1007%2F978-3-030-00668-6_2

Choi, K., Fazekas, G., Sandler, M., Cho, K.  (2018). A Comparison of Audio Signal Preprocessing Methods for Deep Neural Networks on Music Tagging. In: Proc. of the 26th European Signal Processing Conference (EUSIPCO). URL: https://arxiv.org/abs/1709.01922

Choobbasti, A., Gholamian, M., Vaheb, A., and Safavi, S. (2018). JSPEECH: A Multi-lingual conversational speech corpus. In: Proc. of the Speech and Language Technology Workshop (SLT).

Favory, X., Fonseca E., Font F., Serra X. (2018). Facilitating the Manual Annotation of Sounds When Using Large Taxonomies. In: Proc. of the International Workshop on Semantic Audio and the Internet of Things (ISAI), in IEEE FRUCT Conference. URL: https://arxiv.org/abs/1811.10988

Favory, X., Serra, X. (2018). Multi Web Audio Sequencer: Collaborative Music Making. In: Proc. of the Web Audio Conference (WAC). URL: https://webaudioconf.com/papers/multi-web-audio-sequencer-collaborative-music-making.pdf

Ferraro, A., Bogdanov D., Choi K., Serra X. (2018). Using offline metrics and user behavior analysis to combine multiple systems for music recommendation. In: Proc. of the Conference on Recommender Systems (RecSys), REVEAL Workshop. URL: https://drive.google.com/open?id=1_CCCZiyy7J962hcYOO3pqEvtYnd5VPSp

Ferraro, A., Bogdanov D., Yoon J., Kim K. S., Serra X. (2018). Automatic playlist continuation using a hybrid recommender system combining features from text and audio. In: Proc. of the Conference on Recommender Systems (RecSys), Workshop on the RecSys Challenge. URL: https://dl.acm.org/citation.cfm?doid=3267471.3267473

Fonseca, E., Gong R., & Serra X. (2018). A Simple Fusion of Deep and Shallow Learning for Acoustic Scene Classification. In: Proc. of the Sound and Music Computing Conference. URL: https://arxiv.org/abs/1806.07506

Fonseca, E., Plakal M., Font F., Ellis D. P. W., Favory X., Pons J., Serra X. (2018). General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline. In: Proc. of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE). URL: https://arxiv.org/abs/1807.09902

Oramas, S., Bogdanov D., & Porter A. (2018). MediaEval 2018 AcousticBrainz Genre Task: A baseline combining deep feature embeddings across datasets. In: MediaEval Workshop. URL: http://hdl.handle.net/10230/35745

Pearce, A., Brookes, T., Mason, R. (2018). Searching Sound-Effects using Timbre. In: BBC Sounds Amazing.

Safavi, S., Pearce, A., Wang, W., Plumbley, M. (2018). Predicting the perceived level of reverberation using machine learning. In: Proc. of the Asilomar Conference on Signals, Systems, & Computers.

Safavi, S., Wang, W., Plumbley, M., Choobbasti, A., and Fazekas, G. (2018). Predicting the Perceived Level of Reverberationusing Features from Nonlinear Auditory Model. In: Proc. of the International Workshop on Semantic Audio and the Internet of Things (ISAI), in IEEE FRUCT Conference.

Sheng, D., Fazekas, G.  (2018). Feature Design Using Audio Decomposition for Intelligent Control of the Dynamic Range Compressor. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). URL: http://www.mirlab.org/conference_papers/international_conference/ICASSP%202018/pdfs/0000621.pdf

Vaheb, A., Choobbasti, A., Mortazavi, S., and Safavi, S. (2018). Investigating Language Variability on the Performance of Speaker Verification Systems. In: Proc.  of the 21st International Conference on Speech and Computer (SPECOM).

Xambó, A., Roma, G., Lerch, A., Barthet, M., Fazekas, G. (2018). Live Repurposing of Sounds: MIR Explorations with Personal and Crowd-Sourced Databases. In: Proc. of the New Interfaces for Musical Expression (NIME). URL: http://www.musicinformatics.gatech.edu/wp-content_nondefault/uploads/2018/04/Xambo-et-al.-2018-Live-Repurposing-of-Sounds-MIR-Explorations-with-.pdf

* 2019
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Ferraro, A., Bogdanov D., Serra X. (2019). Skip prediction using boosting trees based on acoustic feature of tracks in sessions. In: Proc. of the 12th ACM International Conference on Web Search and Data Mining,  2019 WSDM Cup Workshop.



SUBMITTED, IN PRESS OR PLANNED PAPERS
*************************************

* accepted
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Pearce, A., Brookes, T., Mason, R. (accepted). Modelling Timbral Hardness. In: Journal of Applied Sciences.

* planned
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 (planned). Audio Commons: Achievements and future perspectives (working title). In: -.

* submitted
------

Fonseca, E., Plakal M., Font F., Ellis D. P. W., Favory X., Serra X. (submitted). Learning Sound Event Classifiers from Web Audio with Noisy Labels. In: Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). URL: https://arxiv.org/abs/1901.01189

make_publications_list.py

import csv

AC_PUBLICATIONS_CSV_FILENAME = 'AC publications - Sheet1.csv'

data = csv.DictReader(open(AC_PUBLICATIONS_CSV_FILENAME),
                      fieldnames='Partner,Type,Title,Authors,Year of publications,Title of the Journal or equivalent,Number / date,Publisher,Place of publication,Relevant pages,DOI,ISSN or eSSN,Peer reviewed?,Open access type,Has private participation?,Has been added to the AudioCommons website?,License,Download link,'.split(','))
data = list(data)[2:]  # Skip first rows (header and note)
data = sorted(data, key=lambda x: '{0}-{1}'.format(x['Year of publications'], x['Authors']))  # Sort by year and author

data_journals_books = [pub for pub in data if (pub['Type'] == 'Journal Paper' or pub['Type'] == 'Book chapter') and pub['Year of publications'].isdigit()]
data_conferences = [pub for pub in data if pub['Type'] != 'Journal Paper' and pub['Year of publications'].isdigit()]
data_submitted_planned = [pub for pub in data if not pub['Year of publications'].isdigit()]

def print_publication_for_deliverable(pub):
    print('{0} ({1}). {2}. In: {3}.{4}\n'.format(
        pub['Authors'], 
        pub['Year of publications'], 
        pub['Title'], 
        pub['Title of the Journal or equivalent'], 
        ' URL: {0}'.format(pub['Download link']) if pub['Download link'] != '' else ''))


def print_list(title, pubs):
    print('\n\n{0}\n{1}\n'.format(title, '*'*len(title)))
    current_year = None
    for pub in pubs:
        if pub['Year of publications'] != current_year:
                print('* {0}\n------\n'.format(pub['Year of publications']))
                current_year = pub['Year of publications']
        print_publication_for_deliverable(pub)


print_list('JOURNALS AND BOOK CHAPTERS', data_journals_books)
print_list('CONFERENCE PAPERS', data_conferences)
print_list('SUBMITTED, IN PRESS OR PLANNED PAPERS', data_submitted_planned)