# Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises
## 4 Conclusions  
In this study, the speech enhancement for hearing aids was investigated in actual self-recorded noisy environments. In order to improve speech quality, the environmental noises were classified using convolutional networks, and noise reduction using DNNs was applied bases on the classified noise. The environmental noise was recorded in ten places most related to the environment in which hearing aids are used, and the objective evaluation of speech quality improvement was made using the PESQ, STOI, OQCM, and LLR scores.  
With the proposed algorithm, comprising noise reduction based on noise classification, the PESQ score increased 2.17% in the 0 dB SNR, 3.50% in the 5 dB SNR, 3.69% in the 10 dB SNR, and 2.62% in the 15 dB SNR tests, respectively, than when the classification results were not applied. The STOI score increased 3.23% in the 0 dB SNR, 2.71% in the 5 dB SNR, 1.89% in the 10 dB SNR, and 1.30% in the 15 dB SNR tests, respectively. The OQCM, which was calculated using a combination of the existing objective evaluation measures, increased 0.203 in the 0 dB SNR, 0.243 in the 5 dB SNR, 0.225 in the 10 dB SNR, and 0.161 in the 15 dB SNR tests, respectively. The LLR score was 7.57% lower in the 0 dB SNR, 7.79% lower in the 5 dB SNR, 5.86% lower in the 10 dB SNR, and 4.20% lower in the 15 dB SNR tests, respectively, than the performance of the noise reduction without applying the classification results.  
The proposed speech enhancement of hearing aids provided the best quality of speech in various and irregular noisy environments of the typical hearing aid user. Because recorded actual noises were closer to environments of hearing aid use, noise datasets were effectively used to improve the speech quality for hearing aid users. The speech enhancement using deep learning in hearing aid resulted in the improved speech quality compare with conventional speech enhancement algorithms in hearing aids. In addition, the proposed speech enhancement algorithm, which was applied different DNNs models according to the classified noise, had better performance of speech quality than the case without applying classified noise results. In summary, the increased noise classification rate using CNNs (with two types of image filters) improved the noise reduction performance using DNNs, and through the proposed algorithm, the quality of speech enhancement of the hearing aid improved, resulting in increased listening satisfaction for the people with impaired hearing. As hearing aid chips is advanced and speech signal processing for hearing aids is developed continuously, more complex algorithms could be applied to the hearing aids and more detailed hearing compensation would be provide to the diverse characteristics of hearing.  
Conceptualization, G.P., W.C., K.-S.K. and S.L.; data curation, G.P., W.C., K.-S.K. and S.L.; formal analysis, G.P., W.C., K.-S.K. and S.L.; funding acquisition, S.L.; investigation, G.P., W.C., K.-S.K. and S.L.; methodology, G.P., W.C., K.-S.K. and S.L.; project administration, S.L.; resources, S.L.; software, G.P. and W.C.; supervision, S.L.; validation, G.P., W.C., K.-S.K. and S.L.; visualization, G.P. and W.C.; writing--original draft, G.P., and writing--review and editing, G.P., W.C., K.-S.K. and S.L. All authors have read and agreed to the published version of the manuscript. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2020R1A2C2004624) and supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2018R1A6A1A03025523).  
The authors declare no conflict of interest.