Station audionumérique

Résumé

vignette|Station audionumérique Une station audionumérique (acronyme DAW, de l'anglais Digital Audio Workstation) désigne une station de travail basée sur des composants optimisés pour le pilotage, l'acquisition et le traitement numérique du son. Les stations audionumériques sont conçues pour enregistrer, éditer, manipuler, créer et lire des contenus MIDI et audionumériques. Le concept a évolué avec l'importance prise par l'informatique distribuée au travers de protocoles standards de l'internet. Avec l'évolution des ordinateurs et des réseaux, ces stations sont utilisées tant en musique que dans l'ingénierie du son et l'audiovisuel. Description Une station audionumérique, que les anglo-saxons désignent par l'acronyme « DAW », englobe une combinaison de :

logiciel(s) spécialisé(s) ;
convertisseurs analogique-numérique et numérique-analogique ;
ordinateur(s), éventuellement doté d'une surface de contrôle dédiée ;
disque(s) dur(s) de grande capacité pour l'enregis

Source officielle

https://fr.wikipedia.org/wiki/Station_audionum%C3%A9rique

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Reference current generator, and method of programming, adjusting and/or operating same

Marija Blagojevic, Michel Declercq, Maher Kayal, Marc Pastre, Lionel Portmann

There are many inventions described and illustrated herein. In a first aspect, the present invention is a technique and circuitry for reading data that is stored in memory cells. In one embodiment of this aspect, the present invention is a technique and circuitry for generating a reference current that is used, in conjunction with a sense amplifier, to read data that is stored in memory cells of a DRAM device. The technique and circuitry for generating a reference current may be implemented using an analog configuration, a digital configuration, and/or combinations of analog and digital configurations.

2005

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Analysis and Synthesis of Pseudo-Periodic 1/f-Like Noise by Means of Wavelets with Applications to Digital Audio

Pietro Polotti

2001

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Automatic emotion recognition systems based on supervised machine learning require reliable annotation of affective behaviours to build useful models. Whereas the dimensional approach is getting more and more popular for rating: affective behaviours in continuous time domains, e.g., arousal and valence, methodologies to take into account reaction lags of the human raters are still rare. We therefore investigate the relevance of using machine learning algorithms able to integrate contextual information in the modelling, like long short-term memory recurrent neural networks do, to automatically predict emotion from several (asynchronous) raters in continuous time domains, i.e., arousal and valence. Evaluations are performed on the recently proposed RECOLA multimodal database (27 subjects, 5 min of data and six raters for each), which includes audio, video, and physiological (ECG, EDA) data. In fact, studies uniting audiovisual and physiological information are still very rare. Features are extracted with various window sizes for each modality and performance for the automatic emotion prediction is compared for both different architectures of neural networks and fusion approaches (feature-level/decision-level). The results show that: (i) LSTM network can deal with (asynchronous) dependencies found between continuous ratings of emotion with video data, (ii) the prediction of the emotional valence requires longer analysis window than for arousal and (iii) a decision-level fusion leads to better performance than a feature-level fusion. The best performance (concordance correlation coefficient) for the multimodal emotion prediction is 0.804 for arousal and 0.528 for valence.

Elsevier2015

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