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Automatic tagging using deep convolutional neural networks [article]

Keunwoo Choi, George Fazekas, Mark Sandler
2016 arXiv   pre-print
We present a content-based automatic music tagging algorithm using fully convolutional neural networks (FCNs).  ...  The experiments show that mel-spectrogram is an effective time-frequency representation for automatic tagging and that more complex models benefit from more training data.  ...  Sandler acknowledges the support of the Royal Society as a recipient of a Wolfson Research Merit Award.  ... 
arXiv:1606.00298v1 fatcat:xjk3huankjbqld2rfenivf6coq

Automatic Tagging Using Deep Convolutional Neural Networks

Keunwoo Choi, György Fazekas, Mark B. Sandler
2016 Zenodo  
Sandler acknowledges the support of the Royal Society as a recipient of a Wolfson Research Merit Award.  ...  "Automatic tagging using deep convolutional neural networks', 17th International Society for Music Information Retrieval Conference, 2016.  ...  This approach is known as feature learning and requires deep neural networks (DNNs). Aggregating hand-crafted features for music tagging was introduced in [25] .  ... 
doi:10.5281/zenodo.1416253 fatcat:tvivx4rolfa5rf24ob3smmhayy

Comparing the Accuracy of Deep Neural Networks (DNN) and Convolutional Neural Network (CNN) in Music Genre Recognition (MGR): Experiments on Kurdish Music [article]

Aza Zuhair, Hossein Hassani
2021 arXiv   pre-print
We evaluated two machine learning approaches, a Deep Neural Network (DNN) and a Convolutional Neural Network (CNN), to recognize the genres.  ...  The work on applying AI in the classification of types of music has been growing recently, but there is no evidence of such research on the Kurdish music genres.  ...  In this research, we apply and evaluate the accuracy of two neural network approaches, Deep Neural Network (DNN) and a Convolutional Neural Network (CNN), in automatically classifying Kurdish music into  ... 
arXiv:2111.11063v1 fatcat:gk3xgkudpbdytmreo7dqmnttwq

Arabic Music Genre Classification Using Deep Convolutional Neural Networks (CNNs)

Laiali Almazaydeh, Saleh Atiewi, Arar Al Tawil, Khaled Elleithy
2022 Computers Materials & Continua  
and Mawwal, and finally present a comprehensive empirical comparison of deep Convolutional Neural Networks (CNNs) architectures on Arabic music genres classification.  ...  For this reason, in this research, our objective is first to construct a well-annotated dataset of five of the most well-known Arabic music genres, which are: Eastern Takht, Rai, Muwashshah, the poem,  ...  Using the constructed dataset titled "Ar-MGC: Arabic Music Genre Classification Dataset", we performed a complete empirical comparison of deep CNNs architectures in this study.  ... 
doi:10.32604/cmc.2022.025526 fatcat:csy5spffqja23gwgiodwv3dhlm

The Effects of Noisy Labels on Deep Convolutional Neural Networks for Music Tagging [article]

Keunwoo Choi and George Fazekas and Kyunghyun Cho and Mark Sandler
2017 arXiv   pre-print
Deep neural networks (DNN) have been successfully applied to music classification including music tagging.  ...  Using a trained network, we compute label vector similarities which is compared to groundtruth similarity. The results highlight several important aspects of music tagging and neural networks.  ...  Sandler acknowledges the support of the Royal Society as a recipient of a Wolfson Research Merit Award. Choi acknowledges the support of QMUL Postgraduate Research Fund for research visiting to NYU.  ... 
arXiv:1706.02361v3 fatcat:aio2wo22r5hkfmdjeykj2dymtq

Music Genre Recognition Using Deep Neural Networks and Transfer Learning

Deepanway Ghosal, Maheshkumar H. Kolekar
2018 Interspeech 2018  
In this work we propose a novel approach for music genre recognition using an ensemble of convolutional long short term memory based neural networks (CNN LSTM) and a transfer learning model.  ...  The neural network models are trained on a diverse set of spectral and rhythmic features whereas the transfer learning model was originally trained on the task of music tagging.  ...  In [7] authors introduce a musical transfer learning system. A deep convolutional neural network is first trained on a large dataset [11] for music tagging.  ... 
doi:10.21437/interspeech.2018-2045 dblp:conf/interspeech/GhosalK18 fatcat:lntxb6m5czd5paoqoye74d37ky

Deep Neural Network for Musical Instrument Recognition using MFCCs [article]

Saranga Kingkor Mahanta, Abdullah Faiz Ur Rahman Khilji, Partha Pakray
2021 arXiv   pre-print
In this paper, we use an artificial neural network (ANN) model that was trained to perform classification on twenty different classes of musical instruments.  ...  The task of efficient automatic music classification is of vital importance and forms the basis for various advanced applications of AI in the musical domain.  ...  Acknowledgements We would like to thank the Department of Computer Science and Engineering and Center for Natural Language Processing (CNLP) at National Institute of Technology Silchar for providing the  ... 
arXiv:2105.00933v2 fatcat:yerdreqabzhlppo4xazaz2ir7q

Singing Voice Melody Transcription Using Deep Neural Networks

François Rigaud, Mathieu Radenen
2016 Zenodo  
"Singing Voice Melody Transcription using Deep Neural Networks", 17th International Society for Music Information Retrieval Conference, 2016.  ...  Similarly for noisy speech signals, f 0 estimation algorithms based on Deep Neural Networks (DNN) have been introduced in [9, 12] .  ... 
doi:10.5281/zenodo.1418051 fatcat:76ashwxdbregdjw66kebq4gcxa

Learning Temporal Features Using A Deep Neural Network And Its Application To Music Genre Classification

Il-Young Jeong, Kyogu Lee
2016 Zenodo  
"learning temporal features using a deep neural network and its application to music genre classification", 17th International Society for Music Information Retrieval Conference, 2016.  ...  In this paper, we endeavor to learn TFs using a deep neural network (DNN) from a low-level representation.  ... 
doi:10.5281/zenodo.1416415 fatcat:4gt4bfrny5hjdn7j6tqnpisjfm

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.  ...  Using a dataset of 10k audio excerpts from 11 instruments for evaluation, we found that convolutional neural networks are more robust than conventional methods that exploit spectral features and source  ...  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

Instrument Activity Detection in Polyphonic Music using Deep Neural Networks

Siddharth Gururani, Cameron Summers, Alexander Lerch
2018 Zenodo  
This system allows, for instance, to retrieve specific areas of interest such as guitar solos. Three classes of deep neural networks are trained to detect up to 18 instruments.  ...  The architectures investigated in this paper are: multi-layer perceptrons, convolutional neural networks, and convolutional-recurrent neural networks.  ...  We thank them for their generous support. 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 Forest: Towards An Alternative to Deep Neural Networks

Zhi-Hua Zhou, Ji Feng
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
In this paper, we propose gcForest, a decision tree ensemble approach with performance highly competitive to deep neural networks in a broad range of tasks.  ...  Furthermore, in contrast to deep neural networks which require large-scale training data, gcForest can work well even when there are only small-scale training data.  ...  Music Classification The GTZAN dataset [Tzanetakis and Cook, 2002] contains 10 genres of music clips, each represented by 100 tracks of 30 seconds long.  ... 
doi:10.24963/ijcai.2017/497 dblp:conf/ijcai/ZhouF17 fatcat:63e5y4c6trdtlhu4z2dgngr6am


Klec Mariusz, Korzinek Danijel
2015 Computer Science  
Research described in this paper tries to combine the approach of Deep Neural Networks (DNN) with the novel audio features extracted using the Scattering Wavelet Transform (SWT) for classifying musical  ...  genres.  ...  Krzysztof Marasek, Thomas Kemp, and Christian Scheible for their support. This work was funded by a grant agreement no. ST/MN/ MUL/2013 at the Polish-Japanese Academy of Information Technology.  ... 
doi:10.7494/csci.2015.16.2.133 fatcat:xza6xkrcqrcgpixnpyf6ek4imq

Audio Source Separation Using Deep Neural Networks

Pritish Chandna, Jordi Janer, Marius Miron
2016 Zenodo  
Building on ideas from previous research on source separation, we propose an algorithm using a deep neural network with convolutional layers.  ...  This thesis presents a low latency online source separation algorithm based on convolutional neural networks.  ...  ., 2014] recently proposed a methodology using a deep recurrent neural network for separating singing voice from single channel musical recordings.  ... 
doi:10.5281/zenodo.3755620 fatcat:girvxhgbv5cqplktyzmv22gaqu

Mechanisms of Artistic Creativity in Deep Learning Neural Networks [article]

Lonce Wyse
2019 arXiv   pre-print
The generative capabilities of deep learning neural networks (DNNs) have been attracting increasing attention for both the remarkable artifacts they produce, but also because of the vast conceptual difference  ...  All 5 emerge from machinery built for purposes other than the creative characteristics they exhibit, mostly classification.  ...  Acknowledgements This research was supported by a Singapore MOE Tier 2 grant, "Learning Generative and Parameterized Interactive Sequence Models with Recurrent Neural Networks," (MOE2018-T2-2-127), and  ... 
arXiv:1907.00321v1 fatcat:2xglug6z4vdhpol4rnrnfm7ksu
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