  This study presents a deep learning-based speech signal-processing mobile
application known as CITISEN. The CITISEN provides three functions: speech
enhancement (SE), model adaptation (MA), and background noise conversion (BNC),
allowing CITISEN to be used as a platform for utilizing and evaluating SE
models and flexibly extend the models to address various noise environments and
users. For SE, a pretrained SE model downloaded from the cloud server is used
to effectively reduce noise components from instant or saved recordings
provided by users. For encountering unseen noise or speaker environments, the
MA function is applied to promote CITISEN. A few audio samples recording on a
noisy environment are uploaded and used to adapt the pretrained SE model on the
server. Finally, for BNC, CITISEN first removes the background noises through
an SE model and then mixes the processed speech with new background noise. The
novel BNC function can evaluate SE performance under specific conditions, cover
people's tracks, and provide entertainment. The experimental results confirmed
the effectiveness of SE, MA, and BNC functions. Compared with the noisy speech
signals, the enhanced speech signals achieved about 6\% and 33\% of
improvements, respectively, in terms of short-time objective intelligibility
(STOI) and perceptual evaluation of speech quality (PESQ). With MA, the STOI
and PESQ could be further improved by approximately 6\% and 11\%, respectively.
Finally, the BNC experiment results indicated that the speech signals converted
from noisy and silent backgrounds have a close scene identification accuracy
and similar embeddings in an acoustic scene classification model. Therefore,
the proposed BNC can effectively convert the background noise of a speech
signal and be a data augmentation method when clean speech signals are
unavailable.
