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A holistic approach to polyphonic music transcription with neural networks [article]

Miguel A. Román, Antonio Pertusa, Jorge Calvo-Zaragoza
2019 arXiv   pre-print
We present a framework based on neural networks to extract music scores directly from polyphonic audio in an end-to-end fashion.  ...  For this, we use a Convolutional Recurrent Neural Network (CRNN) with Connectionist Temporal Classification (CTC) loss function which does not require annotated alignments of audio frames with the score  ...  "A holistic approach to polyphonic music transcription with neural networks", 20th International Society for Music Information Retrieval Conference, Delft, The Netherlands, 2019.  ... 
arXiv:1910.12086v1 fatcat:utyjaa4zhzgprfefcnv77x6tae

A Holistic Approach to Polyphonic Music Transcription with Neural Networks

Miguel Roman, Antonio Pertusa, Jorge Calvo-Zaragoza
2019 Zenodo  
We present a framework based on neural networks to extract music scores directly from polyphonic audio in an end-to-end fashion.  ...  For this, we use a Convolutional Recurrent Neural Network (CRNN) with Connectionist Temporal Classification (CTC) loss function which does not require annotated alignments of audio frames with the score  ...  "A holistic approach to polyphonic music transcription with neural networks", 20th International Society for Music Information Retrieval Conference, Delft, The Netherlands, 2019. must be involved such  ... 
doi:10.5281/zenodo.3527914 fatcat:otrre7szwnfl7hifsbdee25ree

PoLyScriber: Integrated Training of Extractor and Lyrics Transcriber for Lyrics Transcription in Polyphonic Music [article]

Xiaoxue Gao, Chitralekha Gupta, Haizhou Li
2022 arXiv   pre-print
Lyrics transcription of polyphonic music is challenging as the background music affects lyrics intelligibility.  ...  in polyphonic music.  ...  E2E model only needs one single neural network with one objective function to optimize for the lyrics transcription task.  ... 
arXiv:2207.07336v2 fatcat:ctnfy23kgze3jlw5qykn3q2w2q

An End-to-end Framework for Audio-to-Score Music Transcription on Monophonic Excerpts

Miguel A. Román, Antonio Pertusa, Jorge Calvo-Zaragoza
2018 Zenodo  
The proposed method is based on a Convolutional Recurrent Neural Network architecture directly trained with pairs of spectrograms and their corresponding symbolic scores in Western notation.  ...  To the best of our knowledge, this is the first automatic music transcription approach which obtains directly a symbolic score from audio, instead of performing separate stages for piano-roll estimation  ...  Polyphonic piano note transcription with recurrent neural networks. In IEEE In- Figure 3 : 3 Evolution curves of the CTC loss, CER, and WER over the validation set with respect to the training epoch.  ... 
doi:10.5281/zenodo.1492337 fatcat:flzhe2sjmbdkhnvvxnthpd34ya

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  ...  This work is supported by A*STAR under its RIE2020 Advanced Manufacturing and Engineering Domain (AME) Programmatic Grant (Grant No. A1687b0033, Project Title: Spiking Neural Networks).  ... 
arXiv:2204.03307v1 fatcat:rf3emyqtfjeuvkdnr24u37eiwe

Polyphonic piano note transcription with recurrent neural networks

Sebastian Bock, Markus Schedl
2012 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
In this paper a new approach for polyphonic piano note onset transcription is presented.  ...  Long Short-Term Memory units are used in a bidirectional neural network to model the context of the notes.  ...  In contrast to those implementations, the neural network of this approach uses a regression output layer.  ... 
doi:10.1109/icassp.2012.6287832 dblp:conf/icassp/BockS12 fatcat:3suzzh7uojbt7abftittlcasdy

Isolated guitar transcription using a deep belief network

Gregory Burlet, Abram Hindle
2017 PeerJ Computer Science  
Music transcription involves the transformation of an audio recording to common music notation, colloquially referred to as sheet music.  ...  This paper presents a polyphonic transcription algorithm that is constrained to processing the audio output of a single instrument, specifically an acoustic guitar.  ...  manual tablature transcriptions to http://www.ultimateguitar.com.  ... 
doi:10.7717/peerj-cs.109 fatcat:q7wv43i4n5gftam254426i4w4e

Joint Multi-Pitch Detection and Score Transcription for Polyphonic Piano Music

Lele Liu, Veronica Morfi, Emmanouil Benetos
2021 ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
Unlike traditional methods that further convert MIDI transcriptions into musical scores, we use a multitask model combined with a Convolutional Recurrent Neural Network and Sequence-to-sequence models  ...  In this paper, we propose a method for joint multi-pitch detection and score transcription for polyphonic piano music.  ...  Another end-to-end A2S system developed in [7, 8, 9] makes use of a convolutional recurrent neural network (CRNN) with connectionist temporal classification (CTC) loss.  ... 
doi:10.1109/icassp39728.2021.9413601 fatcat:wflznncpvjh3djkypfvspoklgi

Skipping the Frame-Level: Event-Based Piano Transcription With Neural Semi-CRFs

Yujia Yan, Frank Cwitkowitz, Zhiyao Duan
2021 Neural Information Processing Systems  
In this work, we propose a novel formulation of piano transcription, which is optimized to directly predict note events.  ...  These practices are not well aligned with the desired outcome of the task, which is the specification of note intervals as holistic events, rather than the aggregation of disjoint observations.  ...  Introduction The task of Automatic Music Transcription (AMT) aims to transcribe a music recording into some form of music notation .  ... 
dblp:conf/nips/YanCD21 fatcat:n67twecknjc53dwb2fqtx3wezq

Separation Of Vocals From Polyphonic Audio Recordings

Shankar Vembu, Stephan Baumann 0001
2005 Zenodo  
This is an enticing application as monophonic transcription is much more simpler when compared to polyphonic music transcription and therefore the harder problem of extracting melody directly using polyphonic  ...  Using Neural Networks We trained a neural network using different combinations of the features namely MFCC, PLP and LFPC.  ... 
doi:10.5281/zenodo.1414851 fatcat:egkwjrqc4na7flc3ihqw2aibsm

End-To-End Optical Music Recognition Using Neural Networks

Jorge Calvo-Zaragoza, Jose J. Valero-Mas, Antonio Pertusa
2017 Zenodo  
FRAMEWORK Our OMR approach is based on a Convolutional Recurrent Neural Network (CRNN) which takes as input an image of a monophonic staff section and directly outputs the sequence of music symbols, with  ...  CONCLUSIONS This work addresses the Optical Music Recognition task in an end-to-end fashion with the use of a Convolutional Recurrent Neural Network (CRNN).  ... 
doi:10.5281/zenodo.1418333 fatcat:b2ie2ekqxferrkg6utiz5mtp2a

Music and connectionism

Brad Garton
1995 Artificial Intelligence  
I would argue for a more holistic approach to musical perception, involving timbre, sonic density, rhythm, time, etc. not as separate musical parameters but instead as essential and interconnected parts  ...  Mark Dolson gives a lucid explanation of some of the basic principles behind neural net operation, complete with an example of a simple network designed to evaluate and classify rudimentary rhythmic patterns  ... 
doi:10.1016/0004-3702(95)90015-2 fatcat:55o2ix6k2bcctjyx62dhabeu3q

End-to-End Neural Optical Music Recognition of Monophonic Scores

Jorge Calvo-Zaragoza, David Rizo
2018 Applied Sciences  
This is achieved by using a neural model that combines the capabilities of convolutional neural networks, which work on the input image, and recurrent neural networks, which deal with the sequential nature  ...  Optical Music Recognition is a field of research that investigates how to computationally decode music notation from images.  ...  Background We study in this work a holistic approach to the task of retrieving the music symbols that appear in score images.  ... 
doi:10.3390/app8040606 fatcat:sqrmtyrg6natng6b6rmmdjyky4

Signal Processing for Music Analysis

Meinard Muller, Daniel P. W. Ellis, Anssi Klapuri, Gaël Richard
2011 IEEE Journal on Selected Topics in Signal Processing  
have been applied to music, often with good results.  ...  Our goal is to demonstrate that, to be successful, music audio signal processing techniques must be informed by a deep and thorough insight into the nature of music itself.  ...  , artificial neural networks, support vector machines, and decision trees [117] , [118] .  ... 
doi:10.1109/jstsp.2011.2112333 fatcat:qvrgekkhzfdkljxn4xbrahg6hu

Computational Methods for Melody and Voice Processing in Music Recordings (Dagstuhl Seminar 19052)

Meinard Müller, Emilia Gómez, Yi-Hsun Yang, Michael Wagner
2019 Dagstuhl Reports  
To cope with the increasing amount of digital music, one requires computational methods and tools that allow users to find, organize, analyze, and interact with music-topics that are central to the research  ...  The Dagstuhl Seminar 19052 was devoted to a branch of MIR that is of particular importance: processing melodic voices (with a focus on singing voices) using computational methods.  ...  Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation.  ... 
doi:10.4230/dagrep.9.1.125 dblp:journals/dagstuhl-reports/MullerGY19 fatcat:w4slm5nxqrdlfaser5dtqar7s4
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