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Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription [article]

Nicolas Boulanger-Lewandowski, Yoshua Bengio
2012 arXiv   pre-print
We introduce a probabilistic model based on distribution estimators conditioned on a recurrent neural network that is able to discover temporal dependencies in high-dimensional sequences.  ...  We investigate the problem of modeling symbolic sequences of polyphonic music in a completely general piano-roll representation.  ...  Acknowledgments The authors would like to thank NSERC, CIFAR and the Canada Research Chairs for funding, and Compute Canada/Calcul Québec for computing resources.  ... 
arXiv:1206.6392v1 fatcat:64ynoazx2baofbjig6qm33qsze

Generative model based polyphonic music transcription

A.T. Cemgil, B. Kappen, D. Barber
2003 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (IEEE Cat. No.03TH8684)  
In this paper we present a model for simultaneous tempo and polyphonic pitch tracking.  ...  It provides a clear framework in which both high level (cognitive) prior information on music structure can be coupled with low level (acoustic physical) information in a principled manner to perform the  ...  In Figure 1 , we show a simple polyphonic score and the corresponding note sequence. The score itself is generated by a score model and is "performed" by an "expressive" sequencer.  ... 
doi:10.1109/aspaa.2003.1285861 fatcat:3r6h3tu5nzbwhm4juxvndkcake

Sequence-to-Sequence Piano Transcription with Transformers [article]

Curtis Hawthorne, Ian Simon, Rigel Swavely, Ethan Manilow, Jesse Engel
2021 arXiv   pre-print
This sequence-to-sequence approach simplifies transcription by jointly modeling audio features and language-like output dependencies, thus removing the need for task-specific architectures.  ...  Automatic Music Transcription has seen significant progress in recent years by training custom deep neural networks on large datasets.  ...  [24] , in this work we treat polyphonic transcription from audio to discrete notes as an end-to-end problem.  ... 
arXiv:2107.09142v1 fatcat:wpenotnebrbsxchhoqcej7to7y

Sequence-to-Sequence Piano Transcription with Transformers

Curtis Hawthorne, Ian Simon, Rigel Swavely, Ethan Manilow, Jesse Engel
2021 Zenodo  
This sequence-to-sequence approach simplifies transcription by jointly modeling audio features and language-like output dependencies, thus removing the need for task-specific architectures.  ...  Automatic Music Transcription has seen significant progress in recent years by training custom deep neural networks on large datasets.  ...  [24] , in this work we treat polyphonic transcription from audio to discrete notes as an end-to-end problem.  ... 
doi:10.5281/zenodo.5624460 fatcat:akixt3awbzbpxphvip2kupxoli

Deep Temporal Sigmoid Belief Networks for Sequence Modeling [article]

Zhe Gan, Chunyuan Li, Ricardo Henao, David Carlson, Lawrence Carin
2015 arXiv   pre-print
Deep dynamic generative models are developed to learn sequential dependencies in time-series data.  ...  Each SBN has a contextual hidden state, inherited from the previous SBNs in the sequence, and is used to regulate its hidden bias.  ...  Acknowledgements This research was supported in part by ARO, DARPA, DOE, NGA and ONR.  ... 
arXiv:1509.07087v1 fatcat:5rpjptihvzefphluybwml4ezzi

A recurrent Markov state-space generative model for sequences

Anand Ramachandran, Steven S. Lumetta, Eric Klee, Deming Chen
2019 International Conference on Artificial Intelligence and Statistics  
In this article, we present a new generative model for sequences that combines both aspects, the ability to perform exact inferences and the ability to model long-term structure, by augmenting the HMM  ...  ) in the generative modeling of music where it outperforms many prominent DNN-based generative models.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.  ... 
dblp:conf/aistats/RamachandranLKC19 fatcat:gytdmcgevnbxblkmx4sh7plstu

Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling [article]

Daniel Stoller, Mi Tian, Sebastian Ewert, Simon Dixon
2019 arXiv   pre-print
However, efficiently modelling long-term dependencies in these sequences is still challenging.  ...  We apply our model ("Seq-U-Net") to a variety of tasks including language and audio generation.  ...  In some of these applications, the sequences can be hundreds of thousands of time-steps in length (e.g. in the case of audio generation due to the high sampling rate of audio signals), and it can be vital  ... 
arXiv:1911.06393v1 fatcat:qcjlahunxzhyjdu5a5rgairjzq

An End-to-End Neural Network for Polyphonic Piano Music Transcription [article]

Siddharth Sigtia, Emmanouil Benetos, Simon Dixon
2016 arXiv   pre-print
The proposed model is general and can be used to transcribe polyphonic music without imposing any constraints on the polyphony.  ...  We present a supervised neural network model for polyphonic piano music transcription.  ...  The proposed model is general and can be used to transcribe polyphonic music without imposing any constraints on the polyphony.  ... 
arXiv:1508.01774v2 fatcat:ktwpjxr3n5b2tjov55efittsei

A Hybrid Recurrent Neural Network For Music Transcription [article]

Siddharth Sigtia, Emmanouil Benetos, Nicolas Boulanger-Lewandowski, Tillman Weyde, Artur S. d'Avila Garcez, Simon Dixon
2014 arXiv   pre-print
We use recurrent neural networks (RNNs) and their variants as music language models (MLMs) and present a generative architecture for combining these models with predictions from a frame level acoustic  ...  The performance of the proposed model is evaluated on piano music from the MAPS dataset and we observe that the proposed model consistently outperforms existing transcription methods.  ...  for modeling sequences of polyphonic music [3] .  ... 
arXiv:1411.1623v1 fatcat:isahmfrvpjbmvmslooo4ta2zla

An End-to-End Neural Network for Polyphonic Piano Music Transcription

Siddharth Sigtia, Emmanouil Benetos, Simon Dixon
2016 IEEE/ACM Transactions on Audio Speech and Language Processing  
The proposed model is general and can be used to transcribe polyphonic music without imposing any constraints on the polyphony.  ...  We present a supervised neural network model for polyphonic piano music transcription.  ...  The proposed model is general and can be used to transcribe polyphonic music without imposing any constraints on the polyphony.  ... 
doi:10.1109/taslp.2016.2533858 fatcat:gtea73lanbhdjpg6wbf3rocjwi

An Auditory Model Based Transcriber Of Singing Sequences

L. P. Clarisse, Jean-Pierre Martens, Micheline Lesaffre, Bernard De Baets, Hans De Meyer, Marc Leman
2002 Zenodo  
This research was supported by project "Musical Audio Mining" (010035-GBOU) which is funded by the Flemish Institute for the Promotion of the Scientific and Technical Research in Industry.  ...  ACKNOWLEDGMENTS We thank Gaetan Martens, Koen Tanghe and Dirk Van Steelant for valuable discussions on the subject. We also acknowledge Emanuele Pollastri for testing his system on our corpus.  ...  (ii) a reliable reference transcription of these sequences, and (iii) a good method for measuring the discrepancies between the generated and the reference transcriptions in a quantitative manner.  ... 
doi:10.5281/zenodo.1416073 fatcat:tpkljq2erjgxldbiam6bag7nny

A hybrid recurrent neural network for music transcription

Siddharth Sigtia, Emmanouil Benetos, Nicolas Boulanger-Lewandowski, Tillman Weyde, Artur S. d'Avila Garcez, Simon Dixon
2015 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
We use recurrent neural networks (RNNs) and their variants as music language models (MLMs) and present a generative architecture for combining these models with predictions from a frame level acoustic  ...  The performance of the proposed model is evaluated on piano music from the MAPS dataset and we observe that the proposed model consistently outperforms existing transcription methods.  ...  for modeling sequences of polyphonic music [3] .  ... 
doi:10.1109/icassp.2015.7178333 dblp:conf/icassp/SigtiaBBWGD15 fatcat:e7htl6kt4vb3pm3sdtdmmauj7e

Using sound to understand protein sequence data: new sonification algorithms for protein sequences and multiple sequence alignments

Edward J. Martin, Thomas R. Meagher, Daniel Barker
2021 BMC Bioinformatics  
We have created five parameter-mapping sonification algorithms that aim to improve knowledge discovery from protein sequences and small protein multiple sequence alignments.  ...  Background The use of sound to represent sequence data—sonification—has great potential as an alternative and complement to visual representation, exploiting features of human psychoacoustic intuitions  ...  Authors' contributions EJM and DB: conceived and designed the research; EJM: performed the research and analysed the data; EJM, DB and TRM: wrote the paper.  ... 
doi:10.1186/s12859-021-04362-7 pmid:34556048 fatcat:pq325wlbanfebgeso6bcbvag3q

Genre-conditioned Acoustic Models for Automatic Lyrics Transcription of Polyphonic Music [article]

Xiaoxue Gao, Chitralekha Gupta, Haizhou Li
2022 arXiv   pre-print
In this work, we propose to transcribe the lyrics of polyphonic music using a novel genre-conditioned network.  ...  Lyrics transcription of polyphonic music is challenging not only because the singing vocals are corrupted by the background music, but also because the background music and the singing style vary across  ...  Acknowledgement This research is supported by the Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funding Scheme (Project No. A18A2b0046).  ... 
arXiv:2204.03307v1 fatcat:rf3emyqtfjeuvkdnr24u37eiwe

An Rnn-Based Music Language Model For Improving Automatic Music Transcription

Siddharth Sigtia, Emmanouil Benetos, Srikanth Cherla, Tillman Weyde, Artur S. D'Avila Garcez, Simon Dixon
2014 Zenodo  
In order to overcome this limitation, models derived from RNNs have been proposed which are better at modelling high-dimensional sequences [9, 26] .  ...  Finally, we would like to extend the current models for high-dimensional sequences to better fit the requirements for music language modelling. c S. Sigtia, E. Benetos, S. Cherla, T. Weyde, A.  ... 
doi:10.5281/zenodo.1416792 fatcat:rsz2b3qh7jh3pes2pziziwtb6m
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