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Learning Multi-modal Similarity
[article]
2010
arXiv
pre-print
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Our experiments here replicate and extend previous work on this data set (McFee and Lanckriet, 2009a) . ...
First, we develop the partial order embedding (POE) framework (McFee and Lanckriet, 2009b) , which allows us to use graph-theoretic algorithms to filter a collection of subjective similarity measurements ...
arXiv:1008.5163v1
fatcat:demnetxmlfadtit6mxbsxfsniy
Hypergraph Models Of Playlist Dialects
2012
Zenodo
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doi:10.5281/zenodo.1415618
fatcat:ecmoyau3gjeybbsduihyz4vnii
Analyzing Song Structure With Spectral Clustering
2014
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McFee, Daniel P.W. ...
Attribution: Brian McFee, Daniel P.W. Ellis. "Analyzing song structure with spectral clustering", 15th International Society for Music Information Retrieval Conference, 2014. ...
doi:10.5281/zenodo.1415778
fatcat:iy6xffzum5hcvkrztmjt47wqqm
The Natural Language Of Playlists
2011
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doi:10.5281/zenodo.1418119
fatcat:vddmegq4ovauxi2i7z4hpq67sq
Learning content similarity for music recommendation
[article]
2011
arXiv
pre-print
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McFee and Lanckriet [10] develop a metric learning algorithm for triplet comparisons as described above. ...
arXiv:1105.2344v1
fatcat:tclay6uy4rahzncoxpsizjfi4a
Heterogeneous Embedding For Subjective Artist Similarity
2009
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doi:10.5281/zenodo.1416283
fatcat:icznfnhs3naz5ikpd4gcyzkon4
Learning Similarity From Collaborative Filters
2010
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doi:10.5281/zenodo.1416198
fatcat:52kdtot5prfjpc5kxcjlmqqf4i
Improving Structure Evaluation Through Automatic Hierarchy Expansion
2019
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Attribution: Brian McFee, Katherine M. Kinnaird. ...
McFee, Katherine M. ...
doi:10.5281/zenodo.3527764
fatcat:xapdvgihhvgrdics7vov2b6gji
A Plan For Sustainable Mir Evaluation
2016
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Attribution: Brian McFee, Eric J. Humphrey, Julián Urbano. "A Plan for Sustainable MIR Evaluation", 17th International Society for Music Information Retrieval Conference, 2016. ...
Each of these components, while requiring some engineering and organizational efforts, are achievable goals with the help of the ISMIR community. c Brian McFee, Eric J. Humphrey, Julián Urbano. ...
doi:10.5281/zenodo.1417775
fatcat:qaerpvtzjvbbpdprdwp6a64f7q
Hierarchical Evaluation Of Segment Boundary Detection
2015
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ACKNOWLEDGEMENTS BM acknowledges support from the Moore-Sloan Data Science Environment at NYU. c Brian McFee, Oriol Nieto, Juan P. Bello. ...
Attribution: Brian McFee, Oriol Nieto, Juan P. Bello. "Hierarchical Evaluation of Segment Boundary Detection", 16th International Society for Music Information Retrieval Conference, 2015. ...
doi:10.5281/zenodo.1414866
fatcat:x3f6ivjdxnentmrlnlijygtja4
Structured Training For Large-Vocabulary Chord Recognition
2017
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Attribution: Brian McFee 1,2 , Juan Pablo Bello 2 . ...
We thank the NVIDIA Corporation for the donation of a Tesla K40 GPU. c Brian McFee 1,2 , Juan Pablo Bello 2 . Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). ...
doi:10.5281/zenodo.1414879
fatcat:k4znkiqsk5dxzjrd7kedmspvua
Collaborative Filtering Based On P2P Networks
2010
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doi:10.5281/zenodo.1415620
fatcat:rnpb7volizb5rm5ypegffdd74m
Large-Scale Music Similarity Search With Spatial Trees
2011
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doi:10.5281/zenodo.1414930
fatcat:riuaxhd5q5ca7kdpmb6shlxnjy
A Software Framework For Musical Data Augmentation
2015
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doi:10.5281/zenodo.1418364
fatcat:oatnhyf5q5ckfgoklkpx6tm4mi
Enhanced Hierarchical Music Structure Annotations via Feature Level Similarity Fusion
[article]
2019
arXiv
pre-print
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We describe a novel pipeline to automatically discover hierarchies of repeated sections in musical audio. The proposed method uses similarity network fusion (SNF) to combine different frame-level features into clean affinity matrices, which are then used as input to spectral clustering. While prior spectral clustering approaches to music structure analysis have pre-processed affinity matrices with heuristics specifically designed for this task, we show that the SNF approach directly yields
arXiv:1902.01023v1
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... ntations which agree better with human annotators, as measured by the "L-measure" metric for hierarchical annotations. Furthermore, the SNF approach immediately supports arbitrarily many input features, allowing us to simultaneously discover structure encoded in timbral, harmonic, and rhythmic representations without any changes to the base algorithm.
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