TechRadar This guy is mapping how instruments radiate sound TechRadar It's been known for centuries that a musical instrument sounds different depending on the direction that you're listening to it from.
Lors d’un récent billet sur Les Carnets de la Phonothèque, Véronique Ginouvès, nous a fait un clin d’oeil en nous lançant sur la piste d’une éventuellement contribution au portail européen de sons liés au travail, Work with sounds.
NB: Ce billet est le premier d’une série à venir, intitulée Enquêtes dans la vallée du Vénéon : sons et rapports à l’environnement, autour de cette thématique générale d’auscultation de l’environnement, et plus précisément autour des enquêtes...
If you need some downtime, you could do worse than heading toward the darkest blue regions on this map. The image shows how the loudness of sound varies across the country, based on 1.5 million hours of acoustical monitoring.
Gizmodo Australia The Noisiest Neighbourhoods In New York, Seattle, And San Francisco Gizmodo Australia Car alarms, jackhammers, barking dogs, drunken brawls outside your window — ah, the sounds of the city.
The acoustic summary as a tool for representing urban sound environments. Damiano Oldonia, , Bert De Coensela, , , Annelies Bockstaela, , Michiel Boesa, , Bernard De Baetsb, , Dick Botteldoorena. Landscape and Urban Planning, Volume 144, December 2015, Pages 34–48.
Detecting and selecting sound events is emerging as an interesting technique for characterizing and representing the sound environment of a specific location. In this article we propose a computational model for automatically constructing a so-called acoustic summary, i.e. a comprehensive collection of sounds aiming to represent the specific sound environment at a given location. Such an acoustic summary could be used by architects, soundscape designers, and urban planners to explore – by listening – the sonic environment at a certain location as it is perceived by a human listener. The model is based on a self-organizing map, a type of neural network. It starts by extracting several psychoacoustic features from the sound. A specific, extensive and unsupervised training allows this map to be tuned to the typical sounds that are likely to be heard at the microphone location. The learning algorithm takes into account some basic aspects of human perception. For example, salient events tend to be better remembered than the ones that do not stand out, even if they occur less frequently. After the training, the self-organizing map is used to form an exhaustive acoustic summary by means of automatically recording specific sound events for the microphone location. In addition to describing the proposed tool, this paper also presents a validation test with local residents in order to show the ability of the model to pick up sounds which bring out the distinctiveness and the specificity of the soundscape as a local resident would do.
Crédits photo : even then. par kevinpaulmorris | Flickr Licence CC-BY-NC A l’occasion de ses recherches pour le laboratoire, la sociologue Martine Leroux s’est intéressée à plusieurs reprises au monde de l’hôpital et...
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