ML Resources

[JorgeRuizDev]

MFCC normalization: https://stats.stackexchange.com/questions/348752/normalization-for-mfcc

Music Datasets: https://ismir.net/resources/datasets/

[JorgeRuizDev]

Transfer Learning for music classification

https://www.researchgate.net/profile/Pratik-Kanani/publication/351379331_Music_Genre_Classification_using_Transfer_Learning_on_log-based_MEL_Spectrogram/links/60dd7490458515d6fbef949d/Music-Genre-Classification-using-Transfer-Learning-on-log-based-MEL-Spectrogram.pdf?origin=publication_detail

[JorgeRuizDev]

https://journals.pan.pl/Content/87690/PDF/eletel-2014-0042.pdf

https://arxiv.org/abs/1703.09179

[JorgeRuizDev]

Another Transfer Learning + Gtzan Paper:
https://www.researchgate.net/publication/320051533_Transfer_Learning_for_Music_Genre_Classification

[JorgeRuizDev]

https://www.researchgate.net/publication/329396097_Music_Genre_Classification_and_Recommendation_by_Using_Machine_Learning_Techniques

https://www.semanticscholar.org/paper/Music-Genre-Classification-using-Machine-Learning-Bahuleyan/2d274145be01169ab329252524cb9314f5f469c1

https://www.analyticsvidhya.com/blog/2021/06/music-genres-classification-using-deep-learning-techniques/

https://github.com/Elxse/deep-learning-for-music-classification-tasks/blob/master/genre-classification/code/6_Machine_Learning_Classifiers.ipynb

https://www.kaggle.com/andradaolteanu/gtzan-dataset-music-genre-classification/code?datasetId=568973&sortBy=voteCount

Classification with Transfer Learning: https://arxiv.org/abs/1703.09179

[JorgeRuizDev]

CNN with Gatzan results

https://arxiv.org/ftp/arxiv/papers/1906/1906.11620.pdf

[JorgeRuizDev]

Emotion Dataset:
http://calab1.ucsd.edu/~datasets/cal500/cal500data/

[JorgeRuizDev]

LSTM Resources
http://www.cs.cuhk.hk/~khwong/p186_acm_00_main_lstm_music_rev5.pdf

[JorgeRuizDev]

Recommending music with DeepLearning:

https://benanne.github.io/2014/08/05/spotify-cnns.html

[JorgeRuizDev]

Movie recommendation system using clustering and pattern recognition network
https://ieeexplore.ieee.org/abstract/document/8301695

[JorgeRuizDev]

The spotify sessions dataset schema:

[JorgeRuizDev]

https://www.geeksforgeeks.org/frequent-item-set-in-data-set-association-rule-mining/

[JorgeRuizDev]

https://arxiv.org/pdf/1901.09851.pdf

[JorgeRuizDev]

Surprise is a Python scikit for building and analyzing recommender systems that deal with explicit rating data

[JorgeRuizDev]

https://acousticbrainz.org/

[JorgeRuizDev]

One VS All

• (https://machinelearningmastery.com/one-vs-rest-and-one-vs-one-for-multi-class-classification/)
• https://scikit-learn.org/stable/modules/multiclass.html

Medidas de error

• distancia de jaccard
• https://www.datascienceblog.net/post/machine-learning/performance-measures-multi-class-problems/

Ensembles

• https://scikit-learn.org/stable/modules/ensemble.html

[JorgeRuizDev]

Big scale music simmilarity
https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.232.2675&rep=rep1&type=pdf