mestrado

Projeto de pesquisa “Detectores acústicos automáticos de espécies de aves noturnas em seu bioma natural” do Departamento de Engenharia Mecânica da Escola Politécnica (Laboratório de Acústica e Meio Ambiente).

Orientador: Prof. Dr. Linilson Padovese

Co-orientador: Prof. Dr. Paulo Hubert

Notas

“There is the need for shared datasets with annotations of a wide variety of calls for a large number of species if methods that are suitable for conservation work are to be developed.” — Automated birdsong recognition in complex acoustic environments: a review

Referências

Fontes de dados

• https://www.xeno-canto.org/ X

• https://www.wikiaves.com.br X

• https://www2.ib.unicamp.br/fnjv/ X

• https://www.imageclef.org/lifeclef/2016/bird

• http://sabiod.univ-tln.fr/nips4b/challenge1.html

• https://www.kaggle.com/c/mlsp-2013-birds

Artigos de bioacústica

• https://pdfs.semanticscholar.org/74e1/fd40d99b811e7a6b2fa897c9d72b471aaf13.pdf

• https://lis-unicamp.github.io/wp-content/uploads/2017/07/LeandroTacioli-Mestrado.pdf (Acesso em 2019-04-14)

• file:///C:/Users/ap_da/OneDrive/Documents/mestrado/ignorar/automated%20birdsong%20review.pdf (Acesso em 2019-05-27)

Algoritmos de identificação de músicas

• https://www.toptal.com/algorithms/shazam-it-music-processing-fingerprinting-and-recognition

• https://royvanrijn.com/blog/2010/06/creating-shazam-in-java/

• https://laplacian.wordpress.com/2009/01/10/how-shazam-works/

• https://github.com/unixjazz/free_shazam

Machine Learning

• https://towardsdatascience.com/recognizing-speech-commands-using-recurrent-neural-networks-with-attention-c2b2ba17c837 (Acesso em 2019-04-14)

• https://github.com/douglas125/SpeechCmdRecognition (Acesso em 2019-04-14)

• https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0179403 (Acesso em 2019-04-14)

• http://www.naturalhistory.com.br/wasis.html (Acesso em 2019-04-14)

• http://c4dm.eecs.qmul.ac.uk/papers/2017/OSA_article_HPamula_etal_04082017.pdf (Acesso em 2019-07-02)

• http://ceur-ws.org/Vol-1609/16090560.pdf (Acesso em 2019-07-06)

• https://arxiv.org/ftp/arxiv/papers/1609/1609.08408.pdf (Acesso em 2019-07-06)

• https://haythamfayek.com/2016/04/21/speech-processing-for-machine-learning.html

• https://blogs.rstudio.com/tensorflow/posts/2019-02-07-audio-background/ (Acesso em 2019-08-01)

Pacotes de R

• https://rethomics.github.io/zeitgebr.html

• https://marce10.github.io/warbleR/

Termos relacionados

• HMM: Hidden Markov Chains
• MFCC: Mel Frequency Cepstral Coefficients
• LPC: Linear Predictive Coding
• LPCC: Linear Prediction Cepstral Coefficients
• PLP: Perceptual Linear Predictive
• Deep Learning