Missing information - Githubissues

ybayle / awesome-deep-learning-music

List of articles related to deep learning applied to music

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Missing information #5

Open ybayle opened 6 years ago

ybayle commented 6 years ago

[ ] 2 missing pdf: Bharucha1988 and Todd1988
[ ] 40 papers are missing the task field
[ ] 82 papers are missing the architecture field
[ ] 84 papers are missing the dataset field

Visualisations:

[ ] Paper evolution graph
[ ] Author clusters thanks to Tulip
[ ] Word cloud

Tips and tricks: http://forums.fast.ai/t/30-best-practices/12344

Unsorted references waiting to be processed: https://github.com/davidwfong/ViolinMelodyCNNs https://www.researchgate.net/publication/325120491_Modeling_Music_Studies_of_Music_Transcription_Music_Perception_and_Music_Production http://www.cs.dartmouth.edu/~sarroff/papers/sarroff2018a.pdf https://www.cs.dartmouth.edu/~sarroff/pages/publications/ https://gitlab.com/rckennedy15/CAPSTONE_2017-2018 https://gitlab.com/kidaa/biaxial-rnn-music-composition https://github.com/chrisdonahue/wavegan https://www.tandfonline.com/doi/full/10.1080/09298215.2018.1458885?af=R https://github.com/Veleslavia/ICMR2017 https://github.com/rupakvignesh/Singing-Voice-Separation https://github.com/tae-jun/resemul http://repository.ubn.ru.nl/bitstream/handle/2066/179506/179506.pdf?sequence=1 http://www.mdpi.com/2076-3417/8/1/150/htm https://arxiv.org/pdf/1511.06939.pdf https://link.springer.com/chapter/10.1007/978-3-319-73600-6_11 https://link.springer.com/chapter/10.1007/978-3-319-73603-7_44 https://arxiv.org/abs/1711.00927 https://arxiv.org/abs/1803.02421 https://arxiv.org/abs/1803.02353 http://jingxixu.com/files/deeplearning.pdf https://arxiv.org/abs/1803.05428 https://www.sciencedirect.com/science/article/pii/S0925231218302431 https://arxiv.org/abs/1705.09792 http://www.apsipa.org/proceedings/2017/CONTENTS/papers2017/15DecFriday/FA-01/FA-01.4.pdf https://arxiv.org/pdf/1611.06265.pdf https://arxiv.org/abs/1802.09221 https://github.com/remyhuang/pop-music-highlighter https://remyhuang.github.io/files/huang17ismir-lbd.pdf https://remyhuang.github.io/files/huang17apsipa.pdf https://github.com/markostam : multiple DL applied to CSI, ... https://link.springer.com/article/10.1007/s11265-018-1334-2 https://www.researchgate.net/publication https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/34703/Ycart%20Polyphonic%20Music%20Sequence%202018%20Accepted.pdf?sequence=3/323184729_BachProp_Learning_to_Compose_Music_in_Multiple_Styles https://www.sciencedirect.com/science/article/pii/S1574954117302467 http://cs229.stanford.edu/proj2017/final-reports/5242716.pdf https://github.com/keunwoochoi/LSTMetallica https://arxiv.org/abs/1802.08370 http://cs229.stanford.edu/proj2017/final-reports/5241796.pdf https://github.com/pawelpeksa/music_emotion_recognition_neuralnets https://arxiv.org/abs/1802.06432 https://arxiv.org/abs/1705.10843 http://cs229.stanford.edu/proj2017/final-reports/5244969.pdf https://github.com/tatsuyah/deep-improvisation http://media.aau.dk/smc/ml4audio/ http://papers.nips.cc/paper/6146-soundnet-learning-sound-representations-from-unlabeled-video.pdf https://github.com/deepsound-project/genre-recognition https://github.com/umbrellabeach/music-generation-with-DL https://github.com/corticph/MSTmodel https://arxiv.org/pdf/1706.09588.pdf https://arxiv.org/abs/1802.05162 https://link.springer.com/article/10.1007/s10844-018-0497-4 https://github.com/devicehive/devicehive-audio-analysis https://arxiv.org/abs/1802.04051 https://arxiv.org/abs/1802.04208 https://magenta.tensorflow.org/onsets-frames dblp.uni-trier.de/db/conf/icmc/icmc2002 (ctrl+f neural network) http://tandfonline.com/doi/full/10.1080/09298215.2017.1367820?af=R& https://arxiv.org/pdf/1801.01589.pdf https://arxiv.org/abs/1802.03144 https://arxiv.org/pdf/1712.00866.pdf https://www.linux.ime.usp.br/~iancarv/mac0499/tcc.pdf https://arxiv.org/abs/1802.06182 https://arxiv.org/abs/1712.05119 https://arxiv.org/abs/1712.05274 https://github.com/jakobabesser/walking_bass_transcription_dnn https://towardsdatascience.com/how-i-created-a-classifier-to-determine-the-potential-popularity-of-a-song-6d63093ba221 https://github.com/ds7711/music_genre_classification https://arxiv.org/abs/1710.10451 https://arxiv.org/abs/1712.07799 https://arxiv.org/pdf/1712.08370.pdf https://arxiv.org/abs/1710.10974 https://arxiv.org/abs/1802.05178 https://arxiv.org/pdf/1703.01789.pdf https://arxiv.org/abs/1711.05772 https://arxiv.org/abs/1801.01589 https://arxiv.org/abs/1712.09668 https://github.com/unnati-xyz/music-generation https://github.com/calclavia/DeepJ and https://arxiv.org/pdf/1801.00887.pdf https://scholar.google.fr/scholar?hl=fr&as_sdt=0%2C5&q=Automatic+Programming+of+VST+Sound+Synthesizers+using+Deep+Networks+and+Other+Techniques+MJ+Yee-King%2C+L+Fedden%2C+M+d%27Inverno&btnG= https://arxiv.org/ftp/arxiv/papers/1712/1712.01011.pdf https://github.com/AI-ON/Few-Shot-Music-Generation https://christophm.github.io/interpretable-ml-book/ https://github.com/dshieble/Music_RNN_RBM https://github.com/feynmanliang/bachbot https://github.com/awjuliani/sound-cnn https://github.com/robbiebarrat/rapping-neural-network https://www.researchgate.net/publication/322977005_Audio_Event_Detection_Using_Multiple-Input_Convolutional_Neural_Network https://arxiv.org/abs/1712.04371 https://arxiv.org/abs/1712.01011 https://arxiv.org/abs/1707.09219 https://arxiv.org/abs/1712.05901 https://arxiv.org/abs/1712.06076 https://arxiv.org/abs/1712.02898 https://arxiv.org/abs/1712.03228 https://arxiv.org/abs/1712.04382 https://arxiv.org/abs/1712.01456 https://arxiv.org/abs/1712.03835 https://arxiv.org/abs/1712.00334 https://arxiv.org/abs/1712.00640 https://arxiv.org/abs/1712.00866 https://arxiv.org/abs/1712.00254 https://arxiv.org/pdf/1712.05119.pdf https://arxiv.org/abs/1712.00166 https://arxiv.org/pdf/1711.11160.pdf https://arxiv.org/pdf/1711.08976.pdf https://github.com/drscotthawley/panotti https://arxiv.org/abs/1703.10847 http://www.music-ir.org/mirex/abstracts/2017/LPNKK1.pdf http://www.music-ir.org/mirex/abstracts/2017/PLNPH1.pdf https://github.com/zhangqianhui/AdversarialNetsPapers https://github.com/LqNoob/MelodyExtraction-MCDNN https://github.com/EdwardLin2014/CNN-with-IBM-for-Singing-Voice-Separation https://github.com/posenhuang/deeplearningsourceseparation https://github.com/minzwon/kakao/blob/master/analyzing.ipynb https://www.researchgate.net/publication/278662921_Deep_Image_Features_in_Music_Information_Retrieval https://arxiv.org/abs/1611.09827v2 https://arxiv.org/abs/1711.08976 https://github.com/kkp15/kkp15.github.io https://www.sciencedirect.com/science/article/pii/S0925231217317666 https://arxiv.org/pdf/1710.11428.pdf (http://mirlab.org:8080/demo/SVSGAN/) www.karindressler.de/papers/dissertation_dressler.pdf https://arxiv.org/abs/1705.09792 http://ieeexplore.ieee.org/abstract/document/8103116/ https://arxiv.org/abs/1709.04384 https://arxiv.org/abs/1711.05772 https://ismir2017.smcnus.org/lbds/Kim2017a.pdf https://arxiv.org/pdf/1711.04845.pdf https://ismir2017.smcnus.org/lbds/Schedl2017.pdf https://lib.ugent.be/fulltxt/RUG01/002/367/502/RUG01-002367502_2017_0001_AC.pdf https://arxiv.org/abs/1412.6596 https://arxiv.org/abs/1706.02361 https://github.com/jthickstun/thickstun2017learning https://arxiv.org/abs/1707.09219 https://arxiv.org/abs/1711.01369 https://arxiv.org/abs/1710.11428 https://arxiv.org/abs/1706.02361 https://arxiv.org/abs/1711.04480 https://arxiv.org/abs/1706.06525 https://github.com/qiuqiangkong/ICASSP2018_audioset https://arxiv.org/abs/1707.05589 https://www.researchgate.net/publication/320632662_Music_Genre_Classification_Using_Masked_Conditional_Neural_Networks https://arxiv.org/pdf/1711.02209.pdf https://arxiv.org/pdf/1711.01369.pdf https://arxiv.org/pdf/1711.00927.pdf https://arxiv.org/abs/1709.06298 https://arxiv.org/abs/1709.04384 https://ismir2017.smcnus.org/lbds/Suh2017.pdf https://ismir2017.smcnus.org/lbds/Pons2017.pdf https://link.springer.com/chapter/10.1007/978-3-319-69911-0_14 https://www.preprints.org/manuscript/201711.0027/v1 https://github.com/Js-Mim https://www.researchgate.net/publication/320867112_Audio_Set_classification_with_attention_model_A_probabilistic_perspective https://arxiv.org/abs/1711.00351 https://arxiv.org/pdf/1710.10451.pdf https://arxiv.org/pdf/1710.11153.pdf http://www.cs.tut.fi/sgn/arg/dcase2017/documents/challenge_technical_reports/DCASE2017_Piczak_208.pdf http://www.cs.tut.fi/sgn/arg/dcase2017/documents/challenge_technical_reports/DCASE2017_Maka_203.pdf https://arxiv.org/abs/1711.00913 https://arxiv.org/abs/1711.00927 https://arxiv.org/find/cs/1/au:+Oord_A/0/1/0/all/0/1 & https://avdnoord.github.io/homepage/vqvae/ https://github.com/qiuqiangkong/ICASSP2018_joint_separation_classification https://www.researchgate.net/publication/320859133_SymCHM-An_Unsupervised_Approach_for_Pattern_Discovery_in_Symbolic_Music_with_a_Compositional_Hierarchical_Model https://www.researchgate.net/publication/315570382_Single_Channel_Audio_Source_Separation_using_Convolutional_Denoising_Autoencoders https://github.com/andabi/music-source-separation https://github.com/andabi/deep-voice-conversion https://arxiv.org/abs/1711.00229 https://arxiv.org/abs/1711.00048 https://arxiv.org/pdf/1710.11549.pdf https://arxiv.org/abs/1711.02209 https://arxiv.org/abs/1710.11473 https://arxiv.org/abs/1710.11428 https://arxiv.org/abs/1710.11418 https://arxiv.org/abs/1710.11385 https://arxiv.org/abs/1710.11153 http://danetapi.com/chimera https://arxiv.org/abs/1710.10451 https://github.com/lamtharnhantrakul/audio_kernels https://github.com/Impro-Visor/lstmprovisor-python https://github.com/hexahedria/biaxial-rnn-music-composition https://github.com/rabitt/ismir2017-deepsalience https://www.researchgate.net/publication/313895490_Comparing_Shallow_versus_Deep_Neural_Network_Architectures_for_Automatic_Music_Genre_Classification https://github.com/marl/crepe https://www.semanticscholar.org/search?year%5B%5D=1991&year%5B%5D=2017&q=deep%20learning%20music%20audio%20neural%20network&sort=relevance http://rodrigob.github.io/are_we_there_yet/build/ https://github.com/syhw/wer_are_we https://www.researchgate.net/publication/320589850_Masked_Conditional_Neural_Networks_for_Audio_Classification https://arxiv.org/pdf/1606.04930.pdf https://github.com/emilylawton/deep-learning-resources https://www.audiolabs-erlangen.de/resources/MIR/2017-GI-Tutorial-Musik/2017_MuellerWeissBalke_GI_DeepLearningMIR.pdf https://books.google.fr/books?hl=fr&lr=&id=1_06DwAAQBAJ&oi=fnd&pg=PA237&ots=QHQvylLIO7&sig=pSqGpvQxa9RUX601lf40mQBPDX8#v=onepage&q&f=false http://cmmr2017.inesctec.pt/wp-content/uploads/2017/09/43_CMMR_2017_paper_31.pdf https://ismir2017.smcnus.org/wp-content/uploads/2017/10/217_Paper.pdf https://ismir2017.smcnus.org/wp-content/uploads/2017/10/77_Paper.pdf https://ismir2017.smcnus.org/wp-content/uploads/2017/10/28_Paper.pdf https://ismir2017.smcnus.org/wp-content/uploads/2017/10/91_Paper.pdf https://ismir2017.smcnus.org/wp-content/uploads/2017/10/17_Paper.pdf https://ismir2017.smcnus.org/wp-content/uploads/2017/10/123_Paper.pdf https://ismir2017.smcnus.org/wp-content/uploads/2017/10/137_Paper.pdf PDF: musicalmetacreation.org/buddydrive/file/smith/ & source : http://musicalmetacreation.org/proceedings__trashed/mume-2017/ https://www.researchgate.net/publication/320519760_Musical_Query-by-Semantic-Description_Based_on_Convolutional_Neural_Network https://www.researchgate.net/publication/314382920_Inside_the_Spectrogram_Convolutional_Neural_Networks_in_Audio_Processing http://ieeexplore.ieee.org/abstract/document/8073570/ https://ismir2017.smcnus.org/wp-content/uploads/2017/10/135_Paper.pdf https://www.researchgate.net/publication/320488483_Acoustic_Scene_Classification_by_Combining_Autoencoder-Based_Dimensionality_Reduction_and_Convolutional_Neural_Networks https://www.mendeley.com/research-papers/deep-multimodal-approach-coldstart-music-recommendation-1/?dgcid=raven_md_feed_email https://www.mendeley.com/research-papers/classification-audio-signals-using-svm-rbfnn-1/?dgcid=raven_md_feed_email https://ismir2017.smcnus.org/wp-content/uploads/2017/10/9_Paper.pdf https://link.springer.com/chapter/10.1007/978-3-319-68121-4_18 https://ismir2017.smcnus.org/wp-content/uploads/2017/10/164_Paper.pdf https://www.researchgate.net/publication/320416485_A_System_for_2017_DCASE_Challenge_Using_Deep_Sequenrial_Image_and_Wavelet_Features?discoverMore=1 https://www.researchgate.net/publication/315100151_Improving_music_source_separation_based_on_deep_neural_networks_through_data_augmentation_and_network_blending https://www.researchgate.net/publication/320333553_Data_augmentation_for_deep_learning_source_separation_of_HipHop_songs https://github.com/karoldvl/paper-2017-DCASE https://repositori.upf.edu/bitstream/handle/10230/32919/Martel_2017.pdf?sequence=1&isAllowed=y https://www.researchgate.net/publication/320333553_Data_augmentation_for_deep_learning_source_separation_of_HipHop_songs?discoverMore=1 https://arxiv.org/abs/1710.04288 http://www.cs.tut.fi/sgn/arg/dcase2017/documents/challenge_technical_reports/DCASE2017_Lee_201.pdf http://www.cs.tut.fi/sgn/arg/dcase2017/documents/challenge_technical_reports/DCASE2017_Yu_188.pdf http://www.semanticaudio.co.uk/wp-content/uploads/2017/09/WIMP2017_Martinez-RamirezReiss.pdf https://arxiv.org/pdf/1609.04243.pdf https://arxiv.org/abs/1711.01634 https://arxiv.org/pdf/1706.02361.pdf https://github.com/RichardYang40148/MidiNet/tree/master/v1 https://ismir2017.smcnus.org/programschedule/ https://twitter.com/keunwoochoi/status/912341967648018435 http://ieeexplore.ieee.org/abstract/document/8049362/ A data-driven model of tonal chord sequence complexity Bruno Di Giorgi ; Simon Dixon ; Massimiliano Zanoni ; Augusto Sarti 2017 https://www.researchgate.net/publication/282997080_A_survey_Time_travel_in_deep_learning_space_An_introduction_to_deep_learning_models_and_how_deep_learning_models_evolved_from_the_initial_ideas https://www.researchgate.net/publication/317265107_Attention_and_Localization_Based_on_a_Deep_Convolutional_Recurrent_Model_for_Weakly_Supervised_Audio_Tagging https://www.researchgate.net/publication/319276246_A_Recurrent_Encoder-Decoder_Approach_With_Skip-Filtering_Connections_for_Monaural_Singing_Voice_Separation https://www.researchgate.net/publication/296704118_Deep_Neural_Networks_for_Dynamic_Range_Compression_in_Mastering_Applications http://c4dm.eecs.qmul.ac.uk/news/news.2016-11-25.C4DM_Seminar_-_Tian_Cheng_and_Siddharth_Sigtia_(Video_Available).html https://arxiv.org/abs/1703.08019 http://slim-sig.irisa.fr/me17/Mediaeval_2017_paper_49.pdf https://groups.csail.mit.edu/sls/publications/2017/YuZhang_PhD_Thesis.pdf https://dl.gi.de/bitstream/handle/20.500.12116/3859/B1-9.pdf?sequence=1&isAllowed=y https://www.meetup.com/fr-FR/Berlin-Music-Information-Retrieval-Meetup/events/243855597/?eventId=243855597 https://www.researchgate.net/publication/315570382_Single_Channel_Audio_Source_Separation_using_Convolutional_Denoising_Autoencoders https://www.researchgate.net/publication/320409290_Wavelets_Revisited_for_the_Classification_of_Acoustic_Scenes https://scholar.google.fr/citations?user=YOY2MFEAAAAJ&hl=fr&oi=sra https://www.researchgate.net/publication/314382920_Inside_the_Spectrogram_Convolutional_Neural_Networks_in_Audio_Processing http://benanne.github.io/2014/08/05/spotify-cnns.html https://vaplab.ee.ncu.edu.tw/english/pcchang/pdf/j52.pdf https://github.com/auDeep/auDeep https://www.cs.tut.fi/sgn/arg/dcase2017/documents/challenge_technical_reports/DCASE2017_Amiriparian_173.pdf https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/25936/QUINTON_Elio_Final_PhD_030817.pdf?sequence=1 https://csmc2017.wordpress.com/proceedings/ http://ofai.at/~jan.schlueter/ https://www.audiolabs-erlangen.de/fau/assistant/balke/publications Deep Learning for Jazz Walking Bass Transcription https://link.springer.com/chapter/10.1007/978-3-319-63450-0_14 https://www.researchgate.net/publication/318030697_Multi-scale_Multi-band_DenseNets_for_Audio_Source_Separation https://www.researchgate.net/publication/282001406_Deep_neural_network_based_instrument_extraction_from_music https://www.researchgate.net/publication/315100151_Improving_music_source_separation_based_on_deep_neural_networks_through_data_augmentation_and_network_blending http://ieeexplore.ieee.org/abstract/document/7994970/ http://www.semanticaudio.co.uk/wp-content/uploads/2017/09/WIMP2017_Martinez-RamirezReiss.pdf https://github.com/search?utf8=%E2%9C%93&q=deep+learning+music&type= https://www.researchgate.net/publication/318030697_Multi-scale_Multi-band_DenseNets_for_Audio_Source_Separation?_esc=Profile%3A%3AInterests&_iepl%5BviewId%5D=1VIp27Fb9rzMbMunG8OwuWAr&_iepl%5BprofilePublicationItemVariant%5D=default&_iepl%5Bcontexts%5D%5B0%5D=prfipi&_iepl%5BtargetEntityId%5D=PB%3A318030697&_iepl%5BinteractionType%5D=publicationTitle https://arxiv.org/abs/1706.07162 https://github.com/oriolromani/MIRdeepLearning https://link.springer.com/chapter/10.1007/978-3-319-68612-7_40 https://arxiv.org/abs/1703.09039 Convolution-based Classification of Audio and Symbolic Representations of Music. Gissel Velarde, Carlos Cancino Chacón, David Meredith, Tillman Weyde and Maarten Grachten. October 22, 2016 (unpublished)

DL4M 2018 articles (to be considered after dealing with 2017): https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8332139 https://arxiv.org/abs/1711.00048 ICASSP2018 https://www.tandfonline.com/doi/full/10.1080/09298215.2018.1438476?af=R https://arxiv.org/abs/1803.04357 https://arxiv.org/abs/1803.04030 https://arxiv.org/abs/1709.01674 https://arxiv.org/abs/1803.06841 https://arxiv.org/abs/1804.04053 https://arxiv.org/abs/1803.06841 https://arxiv.org/abs/1803.08629 https://arxiv.org/abs/1804.00047 https://arxiv.org/abs/1804.00525 https://arxiv.org/ftp/arxiv/papers/1804/1804.02918.pdf https://arxiv.org/abs/1804.04212 http://www.mdpi.com/2076-3417/8/4/507/htm https://arxiv.org/abs/1801.07141 https://arxiv.org/abs/1705.06979 https://arxiv.org/abs/1612.04742 https://www.researchgate.net/publication/322216935_Jazz_music_sub-genre_classification_using_deep_learning https://www.researchgate.net/profile/Loris_Nanni/publication/323938467_Ensemble_of_deep_learning_visual_and_acoustic_features_for_music_genre_classification/links/5ab52e3745851515f599c5da/Ensemble-of-deep-learning-visual-and-acoustic-features-for-music-genre-classification.pdf http://www.mdpi.com/2076-3417/8/4/606/htm https://github.com/pkmital/time-domain-neural-audio-style-transfer https://arxiv.org/abs/1804.07145 https://arxiv.org/abs/1804.07297 https://arxiv.org/abs/1803.01271 https://hal-lirmm.ccsd.cnrs.fr/lirmm-01766781/document https://arxiv.org/abs/1804.07300 https://arxiv.org/abs/1804.07690 https://arxiv.org/abs/1804.08300 https://arxiv.org/abs/1804.08167 https://dl.acm.org/citation.cfm?id=3191822 https://dl.acm.org/citation.cfm?id=3191823 https://arxiv.org/abs/1709.00611 https://arxiv.org/abs/1804.09399 https://arxiv.org/abs/1804.02918 https://arxiv.org/abs/1804.09808 https://arxiv.org/abs/1804.07297 https://dspace.library.uvic.ca/bitstream/handle/1828/9264/Singh_Harpreet_MSc_2018.pdf?sequence=3&isAllowed=y https://arxiv.org/pdf/1804.09202.pdf https://arxiv.org/abs/1805.00237 with https://github.com/jordipons/elmarc https://github.com/NarainKrishnamurthy/BeatGAN2.0 https://arxiv.org/abs/1804.09808 https://github.com/johnglover/sound-rnn https://github.com/NadzeyaKadakova/Studies/blob/master/95-jazznet/Jazz%20Solo%20with%20an%20LSTM%20Network%20.ipynb https://www.politesi.polimi.it/bitstream/10589/139073/1/tesi.pdf https://arxiv.org/abs/1805.02043 https://arxiv.org/abs/1805.02603 https://arxiv.org/abs/1805.03647 https://github.com/gantheory/playlist-cleaning https://arxiv.org/pdf/1805.02410.pdf https://arxiv.org/abs/1803.01271 https://ieeexplore.ieee.org/abstract/document/8356323/ https://arxiv.org/abs/1805.05324 https://marl.smusic.nyu.edu/nieto/publications/TISMIR2018.pdf http://www.aes.org/e-lib/browse.cfm?elib=19513 https://arxiv.org/abs/1805.07848 https://arxiv.org/abs/1805.08559 https://arxiv.org/abs/1805.08501 https://arxiv.org/abs/1805.10808 https://arxiv.org/abs/1804.00525 https://arxiv.org/abs/1805.10548 https://arxiv.org/abs/1805.12176 https://arxiv.org/abs/1806.00195 https://arxiv.org/abs/1801.10492 https://arxiv.org/abs/1806.00509 https://arxiv.org/abs/1806.00770 https://arxiv.org/abs/1806.01180 https://arxiv.org/abs/1805.08559 (https://github.com/sungheonpark/music_source_sepearation_SH_net) https://arxiv.org/abs/1806.08724 https://arxiv.org/abs/1806.08686

Some speech articles: https://arxiv.org/pdf/1710.09798.pdf https://arxiv.org/abs/1804.02918 https://infoscience.epfl.ch/record/203464/files/Palaz_Idiap-RR-18-2014.pdf https://link.springer.com/chapter/10.1007/978-3-319-66429-3_2 https://www.researchgate.net/profile/Cong-Thanh_Do/publication/319269623_Improved_Automatic_Speech_Recognition_Using_Subband_Temporal_Envelope_Features_and_Time-Delay_Neural_Network_Denoising_Autoencoder/links/599f388a4585151e3c6acdd8/Improved-Automatic-Speech-Recognition-Using-Subband-Temporal-Envelope-Features-and-Time-Delay-Neural-Network-Denoising-Autoencoder.pdf https://arxiv.org/pdf/1708.08740.pdf http://newiranians.ir/TASLP2339736-proof.pdf https://asmp-eurasipjournals.springeropen.com/articles/most-recent/rss.xml https://arxiv.org/pdf/1709.00308.pdf https://www.researchgate.net/publication/312520074_A_review_on_Deep_Learning_approaches_in_Speaker_Identification https://www.researchgate.net/publication/317711457_A_Hybrid_Approach_with_Multi-channel_I-Vectors_and_Convolutional_Neural_Networks_for_Acoustic_Scene_Classification https://www.researchgate.net/publication/320180136_Large-scale_weakly_supervised_audio_classification_using_gated_convolutional_neural_network

jordipons commented 5 years ago

I found a paper from 1995 that is not included in the repository.

Kaminskyj, I., & Materka, A. (1995). Automatic source identification of monophonic musical instrument sounds. Proceedings of the IEEE International Conference On Neural Networks,1, (pp. 189-194).

jordipons commented 5 years ago

And I think the work done by Kostek is not included.

Kostek, B. (1995). Feature extraction methods for the intelligent processing of musical sounds. AES 100th convention,Audio Engineering Society.

Kostek, B. (1998). Soft computing-based recognition of musical sounds. In L. Polkowski & A. Skowron (Eds.) Rough Sets in Knowledge Discovery. Heidelberg: Physica-Verlag.

Kostek, B. (1999). Soft computing in acoustics: Applications of neural networks, fuzzy logic and rough sets to musical acoustics. Heidelberg: Physica Verlag.

Kostek, B., & Czyzewski, A. (2000). An approach to the automatic classification of musical sounds. AES 108th convention. Paris: Audio Engineering Society.

Kostek, B., &Czyzewski, A. (2001). Representing musical instrument sounds for their automatic classification. Journal of the Audio Engineering Society, 49, (9) 768-785.

Kostek, B., &Krolikowski, R. (1997). Application of artificial neural networks to the recognition of musical sounds. Archives of Acoustics, 22, (1) 27-50.

Kostek, B., &Wieczorkowska, A. (1997). Parametric representation of musical sounds. Archives of Acoustics, 22, (1) 3-26.

It would be nice to add these early works -- is where the publications are more scarce! :)

ybayle commented 5 years ago

Thanks for your suggestions!