@InProceedings{7362735,
  author = {M. Buccoli and M. Zanoni and F. Setragno and F. Antonacci and A. Sarti},
  booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
  title = {An unsupervised approach to the semantic description of the sound quality of violins},
  year = {2015},
  pages = {2004-2008},
  abstract = {In this study we propose a set of semantic musical descriptors that can be used for describing the timbre of violins. The proposed semantic model follows a dimensional approach, which allows us to express the degree of intensity of each descriptor. A set of recordings of a number of violins (among them, Stradivari, Amati and Guarnieri instruments) were annotated with the descriptors through questionnaires. The recordings are processed with deep learning techniques, to learn salient features from the audio signal in an unsupervised fashion. In this study we propose an automatic annotation procedure based on a set of regression functions that model each semantic descriptor using the learned set of features.},
  keywords = {audio signal processing;feature extraction;learning (artificial intelligence);musical acoustics;musical instruments;regression analysis;violin sound quality;semantic musical descriptors;violins timbre;learning techniques;salient features;audio signal;unsupervised fashion;automatic annotation;regression function;Semantics;Instruments;Training;Neurons;Feature extraction;Europe;Signal processing;High-level music descriptor;violin;timbre;sound quality},
  doi = {10.1109/EUSIPCO.2015.7362735},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570097543.pdf},
}

{"_id":"TLo4mJCCu7CHpLmKr","bibbaseid":"buccoli-zanoni-setragno-antonacci-sarti-anunsupervisedapproachtothesemanticdescriptionofthesoundqualityofviolins-2015","authorIDs":[],"author_short":["Buccoli, M.","Zanoni, M.","Setragno, F.","Antonacci, F.","Sarti, A."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["M."],"propositions":[],"lastnames":["Buccoli"],"suffixes":[]},{"firstnames":["M."],"propositions":[],"lastnames":["Zanoni"],"suffixes":[]},{"firstnames":["F."],"propositions":[],"lastnames":["Setragno"],"suffixes":[]},{"firstnames":["F."],"propositions":[],"lastnames":["Antonacci"],"suffixes":[]},{"firstnames":["A."],"propositions":[],"lastnames":["Sarti"],"suffixes":[]}],"booktitle":"2015 23rd European Signal Processing Conference (EUSIPCO)","title":"An unsupervised approach to the semantic description of the sound quality of violins","year":"2015","pages":"2004-2008","abstract":"In this study we propose a set of semantic musical descriptors that can be used for describing the timbre of violins. The proposed semantic model follows a dimensional approach, which allows us to express the degree of intensity of each descriptor. A set of recordings of a number of violins (among them, Stradivari, Amati and Guarnieri instruments) were annotated with the descriptors through questionnaires. The recordings are processed with deep learning techniques, to learn salient features from the audio signal in an unsupervised fashion. In this study we propose an automatic annotation procedure based on a set of regression functions that model each semantic descriptor using the learned set of features.","keywords":"audio signal processing;feature extraction;learning (artificial intelligence);musical acoustics;musical instruments;regression analysis;violin sound quality;semantic musical descriptors;violins timbre;learning techniques;salient features;audio signal;unsupervised fashion;automatic annotation;regression function;Semantics;Instruments;Training;Neurons;Feature extraction;Europe;Signal processing;High-level music descriptor;violin;timbre;sound quality","doi":"10.1109/EUSIPCO.2015.7362735","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570097543.pdf","bibtex":"@InProceedings{7362735,\n author = {M. Buccoli and M. Zanoni and F. Setragno and F. Antonacci and A. Sarti},\n booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},\n title = {An unsupervised approach to the semantic description of the sound quality of violins},\n year = {2015},\n pages = {2004-2008},\n abstract = {In this study we propose a set of semantic musical descriptors that can be used for describing the timbre of violins. The proposed semantic model follows a dimensional approach, which allows us to express the degree of intensity of each descriptor. A set of recordings of a number of violins (among them, Stradivari, Amati and Guarnieri instruments) were annotated with the descriptors through questionnaires. The recordings are processed with deep learning techniques, to learn salient features from the audio signal in an unsupervised fashion. In this study we propose an automatic annotation procedure based on a set of regression functions that model each semantic descriptor using the learned set of features.},\n keywords = {audio signal processing;feature extraction;learning (artificial intelligence);musical acoustics;musical instruments;regression analysis;violin sound quality;semantic musical descriptors;violins timbre;learning techniques;salient features;audio signal;unsupervised fashion;automatic annotation;regression function;Semantics;Instruments;Training;Neurons;Feature extraction;Europe;Signal processing;High-level music descriptor;violin;timbre;sound quality},\n doi = {10.1109/EUSIPCO.2015.7362735},\n issn = {2076-1465},\n month = {Aug},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570097543.pdf},\n}\n\n","author_short":["Buccoli, M.","Zanoni, M.","Setragno, F.","Antonacci, F.","Sarti, A."],"key":"7362735","id":"7362735","bibbaseid":"buccoli-zanoni-setragno-antonacci-sarti-anunsupervisedapproachtothesemanticdescriptionofthesoundqualityofviolins-2015","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570097543.pdf"},"keyword":["audio signal processing;feature extraction;learning (artificial intelligence);musical acoustics;musical instruments;regression analysis;violin sound quality;semantic musical descriptors;violins timbre;learning techniques;salient features;audio signal;unsupervised fashion;automatic annotation;regression function;Semantics;Instruments;Training;Neurons;Feature extraction;Europe;Signal processing;High-level music descriptor;violin;timbre;sound quality"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2015url.bib","creationDate":"2021-02-13T17:31:52.519Z","downloads":0,"keywords":["audio signal processing;feature extraction;learning (artificial intelligence);musical acoustics;musical instruments;regression analysis;violin sound quality;semantic musical descriptors;violins timbre;learning techniques;salient features;audio signal;unsupervised fashion;automatic annotation;regression function;semantics;instruments;training;neurons;feature extraction;europe;signal processing;high-level music descriptor;violin;timbre;sound quality"],"search_terms":["unsupervised","approach","semantic","description","sound","quality","violins","buccoli","zanoni","setragno","antonacci","sarti"],"title":"An unsupervised approach to the semantic description of the sound quality of violins","year":2015,"dataSources":["eov4vbT6mnAiTpKji","knrZsDjSNHWtA9WNT","oAk4CuFpwvSubKv9g"]}