The phrase "audio background" is correct and usable in written English.
It can be used to refer to sound or music that is played softly in the background while other activities are taking place, such as during a presentation or in a video.
Example: "The video was enhanced by an audio background that created a calming atmosphere for the viewers."
Alternatives: "background sound" or "ambient audio".
Compared to previous ASR evaluations a particular novelty of the task is that the utterances to be recognised were provided in a continuous audio background rather than as pre-segmented utterances thus allowing a range of background modelling techniques to be employed.
In Section 5, the approaches devoted to model audio background are investigated.
The definition of audio background and its modelling for background subtraction incorporates issues that are analogous to those of the visual domain.
In audio, the analog of the waving tree problem is that of a multimodal audio background, in the sense that each independent entity of the model, that is, the audio subband, shows a multimodal statistics.
The structure invariance in the video can be evaluated by employing edge or texture features, while it is not clear neither what is the structure of a environmental audio background, nor what are the features to model it.
Questions of appropriation are always legally thorny; there's no clear point at which audio background noise becomes part of the public domain.
The task was to identify keywords from sentences reverberantly mixed into audio backgrounds binaurally recorded in a busy domestic environment.
This results in the formation of complex and dynamic audio backgrounds.
People with audio research background and people without any special knowledge about audio signal processing were included in the listening tests.
Normal/typical audio events (background soundscape and normal speech) were also provided as input for measuring the abnormality level that our topology characterizes them with.
At its research center in Silicon Valley, chief scientist Andrew Ng used deep learning — feeding large amounts of normal speech audio and background noise to Baidu's AI — to develop Deep Speech, an ASR that is reportedly accurate 81percentt of the time in noisy environments (Apple, Microsoft and Google's best is 65percentt accuracy).
