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How to change phone number in noise Noisefit app? 

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Motivated by these findings, we designed a mobile phone app that alerts deaf and hard-of-hearing people to sounds they care about.
Mobile Phone Sensing offers a great opportunity toward the large scale monitoring of urban phenomena, such as the exposure of the population to environmental noise.
The testing demonstrates how this new way of thinking about noise effect modeling can be used in practice both in management support as a "noise annoyance adviser" and in social science for testing hypotheses such as the effect of noise sensitivity or the degree of urbanization.
This app overcomes the two shortcomings of high latency and musical noise artifact that are associated with the previously developed apps for the same purpose.
Proceedings ArticleDOI
A. Schmid, A. Neubauer 
05 Dec 2005
11 Citations
This paper shows how carrier to noise power estimation can significantly improve the reception sensitivity of Galileo/GPS receivers.
Existence of an asymptotic development of change in threshold hearing sensitivity from long noise exposures has important implications for estimating the hazards of environmental and industrial noise.
Proceedings ArticleDOI
Alin Suciu, Daniel Lebu, Kinga Marton 
20 Oct 2011
16 Citations
We propose an unpredictable random number generator, which, using the hardware sensors of a modern phone, is able to produce high quality sequences of random bits.
Ear-Phone, for the first time, leverages Compressive Sensing to address the fundamental problem of recovering the noise map from incomplete and random samples obtained by crowdsourcing data collection.
This study provides evidence of a behavioral change in sound production of right whales that is correlated with increased noise levels and indicates that right whales may shift call frequency to compensate for increased band-limited background noise.
We propose a people-centric noise collection system called the Ear-Phone.
Experimental results demonstrate that this proposed algorithm yields the much higher speech quality than the state-of-the-art noise-reduction algorithms, and more importantly involves much less computational cost which makes it feasible for mobile phone.
The results of both objective and subjective evaluations are reported which show the effectiveness of this app towards reducing noise for speech signals in noisy environments, in particular in babble background noise.
We concluded that any potential risk of noise-induced hearing loss for mobile phone users should be communicated when users listen regularly, although the volume level was not high enough that the users felt uncomfortable.
The results also show that there is a significant relationship between phone age and its ability to measure noise accurately.
The results of field testing indicate the effectiveness of this unsupervised noise classifier app when used in realistic audio environments.
also show that VoiceLive is robust to different phone placements and is compatible to different sampling rates and phone models.

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