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OpenMIC-2018: An Open Data-set for Multiple Instrument Recognition

Eric Humphrey, Simon Durand, Brian McFee
2018 Zenodo  
This article describes the construction of a new, open data-set for multi-instrument recognition.  ...  We describe in detail how the instrument taxonomy was constructed, how the dataset was sampled and annotated, and compare its characteristics to similar, previous data-sets.  ...  B.M. is supported by the Moore-Sloan Data Science Environment at NYU.  ... 
doi:10.5281/zenodo.1492445 fatcat:vopeeeia25dcrnooamocrihy2e

Visual Attention for Musical Instrument Recognition [article]

Karn Watcharasupat, Siddharth Gururani, Alexander Lerch
2020 arXiv   pre-print
data.  ...  role of pitch and timbre in improving instrument recognition performance.  ...  Recently, an open, moderately-sized dataset for multiple instrument recognition, OpenMIC-2018, was released [2] .  ... 
arXiv:2006.09640v2 fatcat:cwtyxoszdjfungn3hx7cemnao4

Learning Multi-instrument Classification with Partial Labels [article]

Amir Kenarsari Anhari
2020 arXiv   pre-print
Multi-instrument recognition is the task of predicting the presence or absence of different instruments within an audio clip.  ...  A considerable challenge in applying deep learning to multi-instrument recognition is the scarcity of labeled data. OpenMIC is a recent dataset containing 20K polyphonic audio clips.  ...  Name Size Number of Instruments MeledyDB [14] 122 80 MusicNet [15] 330 11 OpenMIC-2018 [13] 20000 20 One challenge of using OpenMIC for multi-instrument recognition is that the dataset is a weakly-labeled  ... 
arXiv:2001.08864v1 fatcat:b6thuwjt5vbvfmc5hb5wcmftwa

Multitask learning for frame-level instrument recognition [article]

Yun-Ning Hung, Yi-An Chen, Yi-Hsuan Yang
2019 arXiv   pre-print
For reproducibility, we will share the code to crawl the data and to implement the proposed model at: instrument-streaming.  ...  We validate the effectiveness of the proposed method for framelevel instrument recognition by comparing it with its singletask ablated versions and three state-of-the-art methods.  ...  OpenMIC-2018 [7] is a new large-scale dataset for training clip-level instrument recognizers. It contains 20,000 10second clilps of Creative Commons-licensed music of various genres.  ... 
arXiv:1811.01143v2 fatcat:ljnum4iqongirlijt24wh4jory

What is the ground truth? Reliability of multi-annotator data for audio tagging [article]

Irene Martin-Morato, Annamaria Mesaros
2021 arXiv   pre-print
Crowdsourcing has become a common approach for annotating large amounts of data.  ...  This raises the problem of data reliability, in addition to the general question of how to combine the opinions of multiple annotators in order to estimate the ground truth.  ...  for instrument recognition [6] or proposed for environmental sound classification in [4] .  ... 
arXiv:2104.04214v1 fatcat:ppxb6lsnojbfnd776umdvaqq3i

Music Classification: Beyond Supervised Learning, Towards Real-world Applications [article]

Minz Won, Janne Spijkervet, Keunwoo Choi
2021 Zenodo  
This is a book written for a tutorial session of the 22nd International Society for Music Information Retrieval Conference, Nov 8-12, 2021 in an online format.  ...  When the classification task has multiple labels, we need to aggregate multiple ROC-AUC scores and PR-AUC scores. In scikit-learn library, there is an option called average.  ...  We hope we also achieved our goals -lowering the barrier of music classification to the newcomers, providing methods to cope with data issues, and narrowing the gap between academia and industry.  ... 
doi:10.5281/zenodo.5703779 fatcat:ggefiongcnb5boahjsz4lgiuz4

Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural Networks

Khaled Koutini, Hamid Eghbal-zadeh, Gerhard Widmer
2021 IEEE/ACM Transactions on Audio Speech and Language Processing  
The proposed CNNs achieve state-of-the-art results in multiple tasks, from acoustic scene classification to emotion and theme detection in music to instrument recognition, as demonstrated by top ranks  ...  An insufficient RF limits the CNN's ability to fit the training data. In contrast, CNNs with an excessive RF tend to over-fit the training data and fail to generalize to unseen testing data.  ...  We thank the members of the Institute of Computational Perception for the useful discussions and feedback.  ... 
doi:10.1109/taslp.2021.3082307 fatcat:5qzi63vw7vemfecalp2t2tja74

Open Praxis vol. 11 issue 2

Inés Gil-Jaurena (ed.), various authors
2019 Open Praxis  
This Open Praxis issue in volume 11 includes eight research papers  ...  Acknowledgments This study was partly supported by Affordable Learning Georgia Grant #277 and an Open Education Research Fellowship (2017)(2018).  ...  Open Praxis, vol. 11 issue 2, April-June 2019, pp. 143-156 Acknowledgements The project "Blended Learning Courses for teacher educators between Asia and Europe" (n°: 574130-EPP-1-2016-1-FR-EPPKA2-CBHE-JP  ... 
doi:10.5944/openpraxis.11.2.999 fatcat:5hwc7sj7k5ednog6edkkbnovru