@InProceedings{8081513,
  author = {I. Sobieraj and Q. Kong and M. D. Plumbley},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Masked Non-negative Matrix Factorization for Bird Detection Using Weakly Labeled Data},
  year = {2017},
  pages = {1769-1773},
  abstract = {Acoustic monitoring of bird species is an increasingly important field in signal processing. Many available bird sound datasets do not contain exact timestamp of the bird call but have a coarse weak label instead. Traditional Non-negative Matrix Factorization (NMF) models are not well designed to deal with weakly labeled data. In this paper we propose a novel Masked Non-negative Matrix Factorization (Masked NMF) approach for bird detection using weakly labeled data. During dictionary extraction we introduce a binary mask on the activation matrix. In that way we are able to control which parts of dictionary are used to reconstruct the training data. We compare our method with conventional NMF approaches and current state of the art methods. The proposed method outperforms the NMF baseline and offers a parsimonious model for bird detection on weakly labeled data. Moreover, to our knowledge, the proposed Masked NMF achieved the best result among non-deep learning methods on a test dataset used for the recent Bird Audio Detection Challenge.},
  keywords = {acoustic signal processing;audio signal processing;learning (artificial intelligence);matrix decomposition;traditional nonnegative matrix factorization models;bird audio detection challenge;novel Masked Nonnegative Matrix Factorization approach;coarse weak label;available bird sound datasets;bird species;Masked NMF;bird detection;weakly labeled data;Birds;Dictionaries;Training;Matrix decomposition;Signal processing;Europe},
  doi = {10.23919/EUSIPCO.2017.8081513},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347329.pdf},
}

{"_id":"xBsiLhBBhhW6wGrct","bibbaseid":"sobieraj-kong-plumbley-maskednonnegativematrixfactorizationforbirddetectionusingweaklylabeleddata-2017","authorIDs":[],"author_short":["Sobieraj, I.","Kong, Q.","Plumbley, M. D."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["I."],"propositions":[],"lastnames":["Sobieraj"],"suffixes":[]},{"firstnames":["Q."],"propositions":[],"lastnames":["Kong"],"suffixes":[]},{"firstnames":["M.","D."],"propositions":[],"lastnames":["Plumbley"],"suffixes":[]}],"booktitle":"2017 25th European Signal Processing Conference (EUSIPCO)","title":"Masked Non-negative Matrix Factorization for Bird Detection Using Weakly Labeled Data","year":"2017","pages":"1769-1773","abstract":"Acoustic monitoring of bird species is an increasingly important field in signal processing. Many available bird sound datasets do not contain exact timestamp of the bird call but have a coarse weak label instead. Traditional Non-negative Matrix Factorization (NMF) models are not well designed to deal with weakly labeled data. In this paper we propose a novel Masked Non-negative Matrix Factorization (Masked NMF) approach for bird detection using weakly labeled data. During dictionary extraction we introduce a binary mask on the activation matrix. In that way we are able to control which parts of dictionary are used to reconstruct the training data. We compare our method with conventional NMF approaches and current state of the art methods. The proposed method outperforms the NMF baseline and offers a parsimonious model for bird detection on weakly labeled data. Moreover, to our knowledge, the proposed Masked NMF achieved the best result among non-deep learning methods on a test dataset used for the recent Bird Audio Detection Challenge.","keywords":"acoustic signal processing;audio signal processing;learning (artificial intelligence);matrix decomposition;traditional nonnegative matrix factorization models;bird audio detection challenge;novel Masked Nonnegative Matrix Factorization approach;coarse weak label;available bird sound datasets;bird species;Masked NMF;bird detection;weakly labeled data;Birds;Dictionaries;Training;Matrix decomposition;Signal processing;Europe","doi":"10.23919/EUSIPCO.2017.8081513","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347329.pdf","bibtex":"@InProceedings{8081513,\n author = {I. Sobieraj and Q. Kong and M. D. Plumbley},\n booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},\n title = {Masked Non-negative Matrix Factorization for Bird Detection Using Weakly Labeled Data},\n year = {2017},\n pages = {1769-1773},\n abstract = {Acoustic monitoring of bird species is an increasingly important field in signal processing. Many available bird sound datasets do not contain exact timestamp of the bird call but have a coarse weak label instead. Traditional Non-negative Matrix Factorization (NMF) models are not well designed to deal with weakly labeled data. In this paper we propose a novel Masked Non-negative Matrix Factorization (Masked NMF) approach for bird detection using weakly labeled data. During dictionary extraction we introduce a binary mask on the activation matrix. In that way we are able to control which parts of dictionary are used to reconstruct the training data. We compare our method with conventional NMF approaches and current state of the art methods. The proposed method outperforms the NMF baseline and offers a parsimonious model for bird detection on weakly labeled data. Moreover, to our knowledge, the proposed Masked NMF achieved the best result among non-deep learning methods on a test dataset used for the recent Bird Audio Detection Challenge.},\n keywords = {acoustic signal processing;audio signal processing;learning (artificial intelligence);matrix decomposition;traditional nonnegative matrix factorization models;bird audio detection challenge;novel Masked Nonnegative Matrix Factorization approach;coarse weak label;available bird sound datasets;bird species;Masked NMF;bird detection;weakly labeled data;Birds;Dictionaries;Training;Matrix decomposition;Signal processing;Europe},\n doi = {10.23919/EUSIPCO.2017.8081513},\n issn = {2076-1465},\n month = {Aug},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347329.pdf},\n}\n\n","author_short":["Sobieraj, I.","Kong, Q.","Plumbley, M. D."],"key":"8081513","id":"8081513","bibbaseid":"sobieraj-kong-plumbley-maskednonnegativematrixfactorizationforbirddetectionusingweaklylabeleddata-2017","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347329.pdf"},"keyword":["acoustic signal processing;audio signal processing;learning (artificial intelligence);matrix decomposition;traditional nonnegative matrix factorization models;bird audio detection challenge;novel Masked Nonnegative Matrix Factorization approach;coarse weak label;available bird sound datasets;bird species;Masked NMF;bird detection;weakly labeled data;Birds;Dictionaries;Training;Matrix decomposition;Signal processing;Europe"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2017url.bib","creationDate":"2021-02-13T16:38:25.700Z","downloads":0,"keywords":["acoustic signal processing;audio signal processing;learning (artificial intelligence);matrix decomposition;traditional nonnegative matrix factorization models;bird audio detection challenge;novel masked nonnegative matrix factorization approach;coarse weak label;available bird sound datasets;bird species;masked nmf;bird detection;weakly labeled data;birds;dictionaries;training;matrix decomposition;signal processing;europe"],"search_terms":["masked","non","negative","matrix","factorization","bird","detection","using","weakly","labeled","data","sobieraj","kong","plumbley"],"title":"Masked Non-negative Matrix Factorization for Bird Detection Using Weakly Labeled Data","year":2017,"dataSources":["2MNbFYjMYTD6z7ExY","uP2aT6Qs8sfZJ6s8b"]}