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Instrument Activity Detection in Polyphonic Music using Deep Neural Networks

Siddharth Gururani, Cameron Summers, Alexander Lerch
2018 Zenodo  
Three classes of deep neural networks are trained to detect up to 18 instruments.  ...  We present an approach for instrument activity detection in polyphonic music with temporal resolution ranging from one second to the track level.  ...  We would also like to thank Nvidia for supporting us with a Titan Xp awarded as part of the GPU grant program.  ... 
doi:10.5281/zenodo.1492479 fatcat:slictqg3xjeydoziwdzc6vqvky

Deep Convolutional Neural Networks for Predominant Instrument Recognition in Polyphonic Music

Yoonchang Han, Jaehun Kim, Kyogu Lee
2017 IEEE/ACM Transactions on Audio Speech and Language Processing  
In this paper, we present a convolutional neural network framework for predominant instrument recognition in real-world polyphonic music.  ...  Identifying musical instruments in polyphonic music recordings is a challenging but important problem in the field of music information retrieval.  ...  His research focuses on signal processing and machine learning techniques applied to music and audio. Lee received a PhD in computerbased music theory and acoustics from Stanford University.  ... 
doi:10.1109/taslp.2016.2632307 fatcat:ykvi5j4cxzbybjjy6acm4oo5du

Transfer Learning for Improving Singing-voice Detection in Polyphonic Instrumental Music [article]

Yuanbo Hou, Frank K. Soong, Jian Luan, Shengchen Li
2020 arXiv   pre-print
Detecting singing-voice in polyphonic instrumental music is critical to music information retrieval.  ...  In this study, clean speech clips with voice activity endpoints and separate instrumental music clips are artificially added together to simulate polyphonic vocals to train a vocal/non-vocal detector.  ...  Deep neural networks [7] are used to estimate an ideal binary spectrogram mask that represents the spectrogram bins in which the vocal is more prominent than the accompaniments.  ... 
arXiv:2008.04658v1 fatcat:2dfxecqo6jadxovo4r5w6g6lhm

Transfer Learning for Improving Singing-Voice Detection in Polyphonic Instrumental Music

Yuanbo Hou, Frank K. Soong, Jian Luan, Shengchen Li
2020 Interspeech 2020  
Detecting singing-voice in polyphonic instrumental music is critical to music information retrieval.  ...  In this study, clean speech clips with voice activity endpoints and separate instrumental music clips are artificially added together to simulate polyphonic vocals to train a vocal /non-vocal detector.  ...  Deep neural networks [7] are used to estimate an ideal binary spectrogram mask that represents the spectrogram bins in which the vocal is more prominent than the accompaniments.  ... 
doi:10.21437/interspeech.2020-1806 dblp:conf/interspeech/HouSLL20 fatcat:5matlrxh55bgrks2a6k3qs3n5u

Vocal Pitch Extraction in Polyphonic Music Using Convolutional Residual Network

Mingye Dong, Jie Wu, Jian Luan
2019 Interspeech 2019  
Especially, due to the presence of accompaniment, vocal pitch extraction in polyphonic music is more challenging.  ...  In addition, shallow networks have been applied on waveform directly, which may not handle contaminated vocal data well. In this paper, a deep convolutional residual network is proposed.  ...  Kum [11] proposed to use multi-column deep neural networks (MCDNN) to predict pitch from spectrogram.  ... 
doi:10.21437/interspeech.2019-2286 dblp:conf/interspeech/DongWL19 fatcat:jdda2ksquben3oyi4omqgf4f7m

Note Detection in Music Teaching Based on Intelligent Bidirectional Recurrent Neural Network

Ya Yue, Muhammad Arif
2022 Security and Communication Networks  
It uses a convolutional neural network (CNN) and a bidirectional long-short-term memory (BiLSTM) network to build a deep neural network model, called convolutional neural network Bidirectional Long Short-Term  ...  First, based on the current research status, a deep neural network model based on CNN and BiLSTM is proposed to detect musical notes.  ...  CNN is a type of deep neural network, and many variants have also appeared in recent years.  ... 
doi:10.1155/2022/8135583 fatcat:ao5gdgbcvvgklktsxmg6bgy5ci

A Single Predominant Instrument Recognition of Polyphonic Music Using CNN-based Timbre Analysis

Daeyeol Kim, Tegg Taekyong Sung, Soo Young Cho, Gyunghak Lee, Chae Bong Sohn
2018 International Journal of Engineering & Technology  
Classifying musical instrument from polyphonic music is a challenging but important task in music information retrieval.  ...  Moreover, for deep learning approach, modified convolutional neural networks (CNN) widely have been researched, but many results have not been improved drastically.  ...  However, it is not simple to detect these characteristics using a computer. In the real world, music is usually played with several different instruments.  ... 
doi:10.14419/ijet.v7i3.34.19388 fatcat:csxdn3ghljgvxexjeocgf43sie

Simultaneous Separation and Transcription of Mixtures with Multiple Polyphonic and Percussive Instruments [article]

Ethan Manilow, Prem Seetharaman, Bryan Pardo
2020 arXiv   pre-print
We present a single deep learning architecture that can both separate an audio recording of a musical mixture into constituent single-instrument recordings and transcribe these instruments into a human-readable  ...  By training each head with different losses, we are able to jointly learn how to separate and transcribe up to 5 instruments in our experiments with a single network.  ...  The new head is used for the automatic transcription of musical mixtures containing multiple polyphonic instruments.  ... 
arXiv:1910.12621v2 fatcat:ci3gdavmpzd4bktkvmn2ec3b3a

Melody Extraction from Polyphonic Music by Deep Learning Approaches: A Review [article]

Gurunath Reddy M and K. Sreenivasa Rao and Partha Pratim Das
2022 arXiv   pre-print
The available deep models have been categorized based on the type of neural network used and the output representation they use for predicting melody.  ...  In this paper, an attempt has been made to review the up-to-date data-driven deep learning approaches for melody extraction from polyphonic music.  ...  Classification based Melody Extraction Models Multi-column deep neural networks are proposed for vocal melody extraction from polyphonic music in [17] .  ... 
arXiv:2202.01078v1 fatcat:ptmc2gl455ezrburr7lvbmpxqq

Multiple F0 Estimation in Vocal Ensembles using Convolutional Neural Networks [article]

Helena Cuesta, Brian McFee, Emilia Gómez
2020 arXiv   pre-print
This paper addresses the extraction of multiple F0 values from polyphonic and a cappella vocal performances using convolutional neural networks (CNNs).  ...  ., all melodic sources are vocals and singers sing in harmony.  ...  Gómez, "Multiple F0 Estimation in Vocal Ensembles using Convolutional Neural Networks", in Proc. of the 21st Int. Society for Music Information Retrieval Conf., Montréal, Canada, 2020.  ... 
arXiv:2009.04172v1 fatcat:mxsjqmmajjfalpjeaf6rlsvy3a

Multiple F0 estimation in vocal ensembles using convolutional neural networks

Helena Cuesta, Brian McFee, Emilia Gomez
2020 Zenodo  
This paper addresses the extraction of multiple F0 values from polyphonic and a cappella vocal performances using convolutional neural networks (CNNs).  ...  ., all melodic sources are vocals and singers sing in harmony.  ...  Gómez, "Multiple F0 Estimation in Vocal Ensembles using Convolutional Neural Networks", in Proc. of the 21st Int. Society for Music Information Retrieval Conf., Montréal, Canada, 2020.  ... 
doi:10.5281/zenodo.4245434 fatcat:b2wpxk4e2vdktks43cpmfh5pvm

An Attention Mechanism for Musical Instrument Recognition

Siddharth Gururani, Mohit Sharma, Alexander Lerch
2019 Zenodo  
Some, such as MedleyDB, have strong per-frame instrument activity annotations but are usually small in size.  ...  We compare the proposed attention model to multiple models which include a baseline binary relevance random forest, recurrent neural network, and fully connected neural networks.  ...  [12] compared various neural network architectures for instrument activity detection using two multi-track datasets containing finegrained instrument activity annotations: MedleyDB and Mixing Secrets  ... 
doi:10.5281/zenodo.3527746 fatcat:3k3s4bucdjcpzgmeoxqklkllq4

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  
Specifically, we make use of the predictions made by a Recurrent Neural Network (RNN) and a RNN-Neural Autore-gressive Distribution Estimator (RNN-NADE) based polyphonic MLM proposed in [9] to refine  ...  Recurrent Neural Network-based models One of the drawbacks of using RNNs to predict polyphonic symbolic music is that any output of the network,ŷ i at time step t, is conditionally independent ofŷ j ,  ... 
doi:10.5281/zenodo.1416792 fatcat:rsz2b3qh7jh3pes2pziziwtb6m

A Comparison of Deep Learning Methods for Timbre Analysis in Polyphonic Automatic Music Transcription

Carlos Hernandez-Olivan, Ignacio Zay Pinilla, Carlos Hernandez-Lopez, Jose R. Beltran
2021 Electronics  
When AMT is faced with deep neural networks, the variety of timbres of different instruments can be an issue that has not been studied in depth yet.  ...  polyphonic music transcription with different timbres with a second approach based on the Deep Salience model that performs polyphonic transcription based on the Constant-Q Transform.  ...  The horizontal axis of the CQT slices in the figure have been rescaled for a better visualization. Figure 5 . 5 Neural Network (NN) structure used in our model for polyphonic transcription.  ... 
doi:10.3390/electronics10070810 fatcat:cpogzlgwofcuniram2vxqejofq

A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and Multipitch Estimation [article]

Rachel M. Bittner, Juan José Bosch, David Rubinstein, Gabriel Meseguer-Brocal, Sebastian Ewert
2022 arXiv   pre-print
In this paper, we propose a lightweight neural network for musical instrument transcription, which supports polyphonic outputs and generalizes to a wide variety of instruments (including vocals).  ...  Storage and network constraints prohibit the use of multiple specialized models, while memory and run-time constraints limit their complexity.  ...  CONCLUSIONS We demonstrate that the proposed low-resource neural network-based model (NMP) can be successfully applied to instrument-agnostic polyphonic note transcription and MPE.  ... 
arXiv:2203.09893v2 fatcat:dqsqaqdmubhy5cwtz765yyv35y
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