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              <p>Noise pollution has a significant impact on quality of life. In indoor soundscapes like open offices, noise exposure creates stress that leads to reduced performance, provokes annoyance and changes in social behaviour. The ReNAR project aims at studying two augmented reality approaches, targeted towards additional sound sources which levels are below or equal to the noise sources ones The first approach tend to conceal the presence of unpleasant sources by adding some spectro-temporal cues which will seemingly convert it into a more pleasant one. Adversarial machine learning techniques will be considered to learn correspondences between noise and pleasing sounds and to train a deep audio synthesiser able to generate an effective concealing sound of moderate loudness. The second approach tend to tackle a common issue encountered in open offices, where the ability to concentrate on the task at hand is made harder when people are speaking nearby. We propose to reduce the intelligibility of nearby speech by the addition of sound sources whose spectro-temporal properties are specifically designed or synthesised with a generative model to conceal important aspects of the nearby speech. The formal position, general frame and expected outcomes of the project will be developed and discussed.</p>
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