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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.
The Precious app takes a unique approach to engage users in the behavior change process by targeting both reflective and spontaneous processes.
Once the NQI is validated, new noise recordings can be evaluated and automobile engineers can estimate how the perceived quality varies as engine working conditions change.
The perfect fit of simulation versus measurements enables deeper analysis and balancing of noise contributors.
This study examines noise in this setting, in turn suggesting how noise levels may be reduced.
We postulate showing how tracker goals relate to other personal fitness goals as key for long-term engagement with trackers.
Accurate measurements of noise exposure and hearing protector fit should improve the ability to model and assess the risks of noise-induced hearing loss.
The results obtained indicate the effectiveness of this real-time smartphone app for carrying out the environmental noise classification in an unsupervised manner.
The results obtained by computing a widely used objective speech quality measure indicate the effectiveness of the app for noise reduction.
The results of field testing indicate the effectiveness of this unsupervised noise classifier app when used in realistic audio environments.