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“The old-fashioned political economist adored, as alone capable of redeeming the human race, the glorious principle of individual greed, although, as this principle requires for its action hypocrisy and fraud, he generally threw in some dash of inconsistent concessions to virtue, as a sop to the vulgar Cerberus. But it is easy to see that the only kind of science this principle would favor would be such as is immediately remunerative with a great preference for such as can be kept secret, like the modern sciences of dyeing and perfumery. Kepler's discovery rendered Newton possible, and Newton rendered modern physics possible, with the steam engine, electricity, and all the other sources of the stupendous fortunes of our age. But Kepler's discovery would not have been possible without the doctrine of conics. Now contemporaries of Kepler -- such penetrating minds as Descartes and Pascal – were abandoning the study of geometry (in which they included what we now call the differential calculus, so far as that had at that time any existence) because they said it was so UTTERLY USELESS. There was the future of the human race almost trembling in the balance; for had not the geometry of conic sections already been worked out in large measure, and had their opinion that only sciences apparently useful ought to be pursued, (sic) [prevailed] the nineteenth century would have had none of those characters which distinguish it from the ancien régime”. Charles Sanders Peirce, Collected Papers of Charles Sanders Peirce, org. por Charles Hartshorne, Paul Weiss, e Arthur W. Burks, 8 vols. (Cambridge, MA: Harvard University Press, 1931-1958), 1.75. “True science is distinctively the study of useless things. For the useful things will get studied without the aid of scientific men. To employ these rare minds on such work is like running a steam engine by burning diamonds”. Ibid., 1.76. “The University of Paris encouraged useless studies in the most effective way possible, by training so many men as to be almost sure of getting a large proportion of all the minds that could be very serviceable in such studies. At the same time, it provided a sure living not only for such as were really successful, but even for those whose talents were of a somewhat inferior kind. On the other hand, like all universities, it set up an official standard of truth, and frowned on all who questioned it. Just so, the German universities for a whole generation turned the cold shoulder to every man who did not extol their stale Hegelianism, until it became a stench in the nostrils of every man of common sense. Then the official fashion shifted, and a Hegelian is today treated in Germany with the same arrogant stupidity with which an anti-Hegelian formerly was. Of course, so-called "universities," whose purpose is not the solution of great problems, but merely the fitting of a selection of young men to earn more money than their fellow citizens not so favored, have for the interests of science none of the value of the medieval and German universities, although they exercise the same baleful influence to about the same degree”. Ibid., 1.77. “The small academies of continental Europe are reasonably free from the gravest fault of the universities. Their defect is that while they indirectly do much for their few members they extend little aid to the younger men, except that of giving a general tone of respectability to pure science”. Ibid., 1.78. “The larger bodies give much less aid to individuals; but they begin to aid them sooner. They have a distinct though limited use when they are specialized, like the Union of German chemists. But whether the Royal Society has been as serviceable to science as the French Académie des Sciences may be doubted”. Ibid., 1.79. Written c. 1896.
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La lezione riguarda la storia greca arcaica nelle sue componenti fondanti. Prima parte.
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Videolezione di Storia (civiltà precolombiane e guerre in Italia nel Cinquecento)
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Link to the associated github repository: https://github.com/turpaultn/Desed Link to the papers: https://hal.inria.fr/hal-02160855, https://hal.inria.fr/hal-02355573v1 Domestic Environment Sound Event Detection (DESED). Description This dataset is the synthetic part of the DESED dataset. It allows creating mixtures of isolated sounds and backgrounds. There is the material to: Reproduce the DCASE 2019 task 4 synthetic dataset Reproduce the DCASE 2020 task 4 synthetic train dataset Creating new mixtures from isolated foreground sounds and background sounds. Files: If you want to generate new audio mixtures yourself from the original files. DESED_synth_soundbank.tar.gz : Raw data used to generate mixtures. DESED_synth_dcase2019jams.tar.gz: JAMS files, metadata describing how to recreate the dcase2019 synthetic dataset DESED_synth_dcase20_train_jams.tar: JAMS files, metadata describing how to recreate the dcase2020 synthetic train dataset DESED_synth_source.tar.gz: src files you can find on github: https://github.com/turpaultn/DESED . Source files to generate dcase2019 files from soundbank or generate new ones. (code can be outdated here, recommended to go in the github repo) If you simply want the evaluation synthetic dataset used in DCASE 2019 task 4. DESED_synth_eval_dcase2019.tar.gz : Evaluation audio and metadata files used in dcase 2019 task 4. The mixtures are generated using Scaper (https://github.com/justinsalamon/scaper) [1]. * Background files are extracted from SINS [2], MUSAN [3] or Youtube and have been selected because they contain a very low amount of our sound event classes. * Foreground files are extracted from Freesound [4][5] and manually verified to check the quality and segmented to remove silences. References [1] J. Salamon, D. MacConnell, M. Cartwright, P. Li, and J. P. Bello. Scaper: A library for soundscape synthesis and augmentation In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz, NY, USA, Oct. 2017. [2] Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers. The SINS database for detection of daily activities in a home environment using an acoustic sensor network. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), 32–36. November 2017. [3] David Snyder and Guoguo Chen and Daniel Povey. MUSAN: A Music, Speech, and Noise Corpus. arXiv, 1510.08484, 2015. [4] F. Font, G. Roma & X. Serra. Freesound technical demo. In Proceedings of the 21st ACM international conference on Multimedia. ACM, 2013. [5] E. Fonseca, J. Pons, X. Favory, F. Font, D. Bogdanov, A. Ferraro, S. Oramas, A. Porter & X. Serra. Freesound Datasets: A Platform for the Creation of Open Audio Datasets. In Proceedings of the 18th International Society for Music Information Retrieval Conference, Suzhou, China, 2017.
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Videolezione sulla Rivoluzione europea del 1848
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Link to the associated github repository: https://github.com/turpaultn/Desed Link to the papers: https://hal.inria.fr/hal-02160855, https://hal.inria.fr/hal-02355573v1 Domestic Environment Sound Event Detection (DESED). Description This dataset is the synthetic part of the DESED dataset. It allows creating mixtures of isolated sounds and backgrounds. There is the material to: Reproduce the DCASE 2019 task 4 synthetic dataset Reproduce the DCASE 2020 task 4 synthetic train dataset Creating new mixtures from isolated foreground sounds and background sounds. Files: If you want to generate new audio mixtures yourself from the original files. DESED_synth_soundbank.tar.gz : Raw data used to generate mixtures. DESED_synth_dcase2019jams.tar.gz: JAMS files, metadata describing how to recreate the dcase2019 synthetic dataset DESED_synth_dcase20_train_jams.tar: JAMS files, metadata describing how to recreate the dcase2020 synthetic train dataset DESED_synth_source.tar.gz: src files you can find on github: https://github.com/turpaultn/DESED . Source files to generate dcase2019 files from soundbank or generate new ones. (code can be outdated here, recommended to go in the github repo) If you simply want the evaluation synthetic dataset used in DCASE 2019 task 4. DESED_synth_eval_dcase2019.tar.gz : Evaluation audio and metadata files used in dcase 2019 task 4. The mixtures are generated using Scaper (https://github.com/justinsalamon/scaper) [1]. * Background files are extracted from SINS [2], MUSAN [3] or Youtube and have been selected because they contain a very low amount of our sound event classes. * Foreground files are extracted from Freesound [4][5] and manually verified to check the quality and segmented to remove silences. References [1] J. Salamon, D. MacConnell, M. Cartwright, P. Li, and J. P. Bello. Scaper: A library for soundscape synthesis and augmentation In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz, NY, USA, Oct. 2017. [2] Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers. The SINS database for detection of daily activities in a home environment using an acoustic sensor network. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), 32–36. November 2017. [3] David Snyder and Guoguo Chen and Daniel Povey. MUSAN: A Music, Speech, and Noise Corpus. arXiv, 1510.08484, 2015. [4] F. Font, G. Roma & X. Serra. Freesound technical demo. In Proceedings of the 21st ACM international conference on Multimedia. ACM, 2013. [5] E. Fonseca, J. Pons, X. Favory, F. Font, D. Bogdanov, A. Ferraro, S. Oramas, A. Porter & X. Serra. Freesound Datasets: A Platform for the Creation of Open Audio Datasets. In Proceedings of the 18th International Society for Music Information Retrieval Conference, Suzhou, China, 2017.
Data Types:
  • Video
Link to the associated github repository: https://github.com/turpaultn/Desed Link to the papers: https://hal.inria.fr/hal-02160855, https://hal.inria.fr/hal-02355573v1 Domestic Environment Sound Event Detection (DESED). Description This dataset is the synthetic part of the DESED dataset. It allows creating mixtures of isolated sounds and backgrounds. There is the material to: Reproduce the DCASE 2019 task 4 synthetic dataset Reproduce the DCASE 2020 task 4 synthetic train dataset Creating new mixtures from isolated foreground sounds and background sounds. Files: If you want to generate new audio mixtures yourself from the original files. DESED_synth_soundbank.tar.gz : Raw data used to generate mixtures. DESED_synth_dcase2019jams.tar.gz: JAMS files, metadata describing how to recreate the dcase2019 synthetic dataset DESED_synth_dcase20_train_jams.tar: JAMS files, metadata describing how to recreate the dcase2020 synthetic train dataset DESED_synth_source.tar.gz: src files you can find on github: https://github.com/turpaultn/DESED . Source files to generate dcase2019 files from soundbank or generate new ones. (code can be outdated here, recommended to go in the github repo) If you simply want the evaluation synthetic dataset used in DCASE 2019 task 4. DESED_synth_eval_dcase2019.tar.gz : Evaluation audio and metadata files used in dcase 2019 task 4. The mixtures are generated using Scaper (https://github.com/justinsalamon/scaper) [1]. * Background files are extracted from SINS [2], MUSAN [3] or Youtube and have been selected because they contain a very low amount of our sound event classes. * Foreground files are extracted from Freesound [4][5] and manually verified to check the quality and segmented to remove silences. References [1] J. Salamon, D. MacConnell, M. Cartwright, P. Li, and J. P. Bello. Scaper: A library for soundscape synthesis and augmentation In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz, NY, USA, Oct. 2017. [2] Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers. The SINS database for detection of daily activities in a home environment using an acoustic sensor network. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), 32–36. November 2017. [3] David Snyder and Guoguo Chen and Daniel Povey. MUSAN: A Music, Speech, and Noise Corpus. arXiv, 1510.08484, 2015. [4] F. Font, G. Roma & X. Serra. Freesound technical demo. In Proceedings of the 21st ACM international conference on Multimedia. ACM, 2013. [5] E. Fonseca, J. Pons, X. Favory, F. Font, D. Bogdanov, A. Ferraro, S. Oramas, A. Porter & X. Serra. Freesound Datasets: A Platform for the Creation of Open Audio Datasets. In Proceedings of the 18th International Society for Music Information Retrieval Conference, Suzhou, China, 2017.
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Video on farmers favorising biodiversity
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Videolezioni di Storia
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phacofragmentation
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