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Automatic identification of music performers with learning ensembles

Efstathios Stamatatos, Gerhard Widmer
2005 Artificial Intelligence  
The proposed ensemble can efficiently cope with multi-class music performer recognition under inter-piece conditions, a difficult musical task, displaying a level of accuracy unlikely to be matched by  ...  This article addresses the problem of identifying the most likely music performer, given a set of performances of the same piece by a number of skilled candidate pianists.  ...  We are indebted to the anonymous reviewers for very helpful comments and, in particular, the suggestion to perform the MRR experiment reported in Table 6 .  ... 
doi:10.1016/j.artint.2005.01.007 fatcat:infpokoylvcrjhjgocjrb3s6ti

Automatic Performer Identification in Celtic Violin Audio Recordings

Rafael Ramirez, Esteban Maestre, Alfonso Perez, Xavier Serra
2011 Journal of New Music Research  
We present a machine learning approach to the problem of identifying performers from their interpretative styles.  ...  Our results indicate that the features extracted contain sufficient information to distinguish the considered performers, and the explored machine learning methods are capable of learning the expressive  ...  Conclusions In this paper, we concentrated on the task of automatic identification of violin performers based on their playing style.  ... 
doi:10.1080/09298215.2011.572171 fatcat:syllq3j43jaw3kss3bsu7vttea

Domestic Cat Sound Classification Using Transfer Learning

Yagya Raj Pandeya, Joonwhoan Lee
2018 International Journal of Fuzzy Logic and Intelligent Systems  
Extracted feature are input to six various classifiers and ensemble techniques applied with predicted probabilities of all classifier results.  ...  The ensemble and data augmentation perform better in this research. Finally, various results are evaluated using confusion matrix and receiver operating characteristic curve.  ...  Acknowledgements The research leading to these result, authors would like to thank Korean Ministry of Education for funding.  ... 
doi:10.5391/ijfis.2018.18.2.154 fatcat:qszioyaio5dk7daeiw6tmnktoq

Cover Song Recognition Using Machine Learning Techniques

Andree Silva-Reyes, Fabiola Martínez-Licona, Alma Martínez Licona
2018 Research in Computing Science  
According to the results, a system that integrates a frequency processing on the pitches with beat alignment, a sparse codification and a clustering technique was obtained with correct cover identification  ...  It was also possible to get information about learning techniques combinations with different metrics that allows future experiments to improve the results.  ...  Introduction The intersection among music, machine learning and signal processing has let to address a wide range of task such as automatic definition of melodies, chords and instruments, identification  ... 
doi:10.13053/rcs-147-4-1 fatcat:jn3fniknxzeermocj5masfgs5q

An Interactive Workflow for Generating Chord Labels for Homorhythmic Music in Symbolic Formats

Yaolong Ju, Samuel Howes, Cory McKay, Nathaniel Condit-Schultz, Jorge Calvo-Zaragoza, Ichiro Fujinaga
2019 Zenodo  
It combines the consistency of rule-based models with the nuance of manual analysis to generate relatively inexpensive high-quality ground truth for training effective machine learning models.  ...  of machine learning models.  ...  Experiment 2 Experiment 2 compared the performance of the classifier ensemble after fully automated training (Analysis 1 in Fig. 2) with that of the ensemble after human-assisted re-training (Analysis  ... 
doi:10.5281/zenodo.3527950 fatcat:ymm3icaufnhxdbbtvjxkdbgyiq

Automatic Music Genre Classification of Audio Signals with Machine Learning Approaches

Y.M.D. Chathuranga, K.L. Jayaratne
2013 GSTF International Journal on Computing  
We observed higher classification accuracies with the ensembles, than with the individual classifiers and improvements of the performances on the GTZAN and ISMIR2004 genre datasets are three percent on  ...  This paper presents a comprehensive machine learning approach to the problem of automatic musical genre classification using the audio signal.  ...  Section IV deals with feature selection, the automatic musical genre classification using different machine learning algorithms and evaluation of the proposed ensemble methods and finally, Section V provides  ... 
doi:10.5176/2251-3043_3.2.251 fatcat:b4rf2324qbg5vlzb2sbgjwjxce

Instrument Identification In Solo And Ensemble Music Using Independent Subspace Analysis

Emmanuel Vincent, Xavier Rodet
2004 Zenodo  
PERFORMANCE ON ENSEMBLE MUSIC Finally the performance of the method was tested on ensemble music.  ...  Learning Instrument models can be learnt from a large variety of learning excerpts, ranging from isolated notes to ensemble music.  ... 
doi:10.5281/zenodo.1416523 fatcat:qucbpeal3ra7bn7ahtrgj36qji

Ensemble of convolutional neural networks to improve animal audio classification

Loris Nanni, Yandre M. G. Costa, Rafael L. Aguiar, Rafael B. Mangolin, Sheryl Brahnam, Carlos N. Silla
2020 EURASIP Journal on Audio, Speech, and Music Processing  
Finally, the ensemble of CNNs is combined with handcrafted texture descriptors obtained from spectrograms for further improvement of performance.  ...  We present an ensemble of classifiers that performs competitively on different types of animal audio datasets using the same set of classifiers and parameter settings.  ...  Acknowledgements We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.  ... 
doi:10.1186/s13636-020-00175-3 fatcat:ftol6ozepbburcyi37dswpqodu

Automatic Music Genre Classification of Audio Signals with Machine Learning Approaches

Dhanith Chathuranga, Lakshman Jayaratne
2013 GSTF International Journal on Computing  
We observed higher classification accuracies with the ensembles, than with the individual classifiers and improvements of the performances on the GTZAN and ISMIR2004 genre datasets are three percent on  ...  This paper presents a comprehensive machine learning approach to the problem of automatic musical genre classification using the audio signal.  ...  Section IV deals with feature selection, the automatic musical genre classification using different machine learning algorithms and evaluation of the proposed ensemble methods and finally, Section V provides  ... 
doi:10.7603/s40601-013-0014-0 fatcat:x4g4qj53mjdw5gg3jyg2wkpx5m

Jazz Solo Instrument Classification with Convolutional Neural Networks, Source Separation, and Transfer Learning

Juan S. Gómez, Jakob Abeßer, Estefanía Cano
2018 Zenodo  
Our results indicate that both source separation as pre-processing step as well as transfer learning clearly improve recognition performance, especially for smaller subsets of highly similar instruments  ...  For the particular use-case of solo instrument recognition in jazz ensemble recordings, we further apply transfer learning techniques to fine-tune a previously trained instrument recognition model for  ...  This information is valuable for a variety of tasks such as automatic music transcription, source separation, music similarity computation, and music recommendation, among others.  ... 
doi:10.5281/zenodo.1492481 fatcat:d5yj7hqb6rhrjpbvwodi2xc62u

Transformer-based ensemble method for multiple predominant instruments recognition in polyphonic music

Lekshmi Chandrika Reghunath, Rajeev Rajan
2022 EURASIP Journal on Audio, Speech, and Music Processing  
The architectural choice of transformers with ensemble voting on Mel-spectro-/modgd-/tempogram has merit in recognizing the predominant instruments in polyphonic music.  ...  The performance of the proposed system is compared with that of the state-of-the-art Han's model, convolutional neural networks (CNN), and deep neural networks (DNN).  ...  Bosch, Ferdinand Fuhrmann, and Perfecto Herrera ( Music Technology Group -Universitat Pompeu Fabra) for developing the IRMAS dataset and making it publicly available.  ... 
doi:10.1186/s13636-022-00245-8 fatcat:nvlrbz2y3new7mwquafljfxyie

Domestic Cat Sound Classification Using Learned Features from Deep Neural Nets

Yagya Raj Pandeya, Dongwhoon Kim, Joonwhoan Lee
2018 Applied Sciences  
In this paper, we deal with the automatic classification of cat sounds using machine learning.  ...  For the classification, we exploited five different machine learning algorithms and an ensemble of them.  ...  Acknowledgments: The research leading to these results, authors would like to thank Korean Ministry of Education and Ministry of Trade, Industry and Energy for their funding.  ... 
doi:10.3390/app8101949 fatcat:o47ahammo5cxzcrmnzaat5b7gq

Duet Interaction: Learning Musicianship For Automatic Accompaniment

Guangyu Xia, Roger Dannenberg
2015 Zenodo  
In this paper, we explore the possibility of learning some basic music performance skills from rehearsal data.  ...  We have built an artificial pianist that can automatically improve its ability to sense and coordinate with a human pianist, learning from rehearsal experience.  ...  , explore the It is well known that musicians in ensembles interact with each properties of the learned models, such as dominant features, limits of other to achieve  ... 
doi:10.5281/zenodo.1179197 fatcat:4jxsswilv5dqtawa3mdqrf35qu

A Comparison Study to Identify Birds Species Based on Bird Song Signals

Xin Guo, Qing-Zhong Liu, L. Long, Y. Li, X. Li, Y. Dai, H. Yang
2017 ITM Web of Conferences  
Our experimental results show that in the comparison study, ensemble learning using discriminant learner with the integration of MFCC features and geo meta-features obtains the best detection performance  ...  The learning classifiers Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Ensemble Learning are examined.  ...  Acknowledgements The support for this study from the college of science and engineering technology of the sam houston state university (shsu), and from the shsu computer science department is highly appreciated  ... 
doi:10.1051/itmconf/20171202002 fatcat:dzk4hkyxzzexhaj6zbjbq6sss4

Ace: A Framework For Optimizing Music Classification

Cory McKay, Rebecca Fiebrink, Daniel McEnnis, Beinan Li, Ichiro Fujinaga
2005 Zenodo  
ACKNOWLEDGEMENTS The generous financial support from the Social Sciences and Humanities Research Council of Canada, the Centre for Interdisciplinary Research in Music, Media and Technology (CIRMMT) and  ...  These include genre classification, similarity analysis, music recommendation, performer identification, composer identification and instrument identification, to name just a few.  ...  One of the main features of ACE is that it automatically performs experiments with these approaches and their various parameters in order to find those that are well suited to each problem's particular  ... 
doi:10.5281/zenodo.1415720 fatcat:hitptasryrehlaw3p5lvntlfve
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