It is essential but quite challenging to alleviate speech information loss and distortion while developing the speech processing algorithms in hearing aids. Recently, many speech enhancement methods based on deep learning are proven effective. However, most of the algorithms fail to achieve real-time processing, which is significant for hearing aids, especially for a smartphone-centered binaural hearing aid system. A supervised speech enhancement method based on an RNN structure is proposed to address the real-time problem. The problem is explored as a resource-constrained speech intelligibility improvement problem with the target of improving speech intelligibility at low SNR situations. Both the objective and subjective experimental results, using the standard evaluation metrics and the experiments on volunteers, respectively, have verified the superiority of the proposed method.

This paper presents a smartphone-based binaural hearing aid architecture for improving the speech intelligibility of hearing aid users. The proposed system consists of an earpiece, a smartphone and an application that performs real-time speech enhancement. The speaker’s voice, which is picked up by the microphone of the earpiece that is worn on the ear, is transmitted to the smartphone via wireless technology. After the speech intelligibility is improved in real time by the deep learning speech enhancement application, it is returned to the earpiece and generates sound. Deep learning speech enhancement algorithms can be used without performing burdensome calculations on the processors in the hearing aid. The results showed that the average usage of the central processing unit in the smartphone was approximately 26%, and the signal-to-noise ratios improve by at least 20%. The presented objective and subjective results show that the proposed method achieves comparatively more noise suppression without distorting the audio.

This paper presents a new structure for hearing aids. Normally, the power consumption and user experience are contradictory. The proposed hearing aid structure mainly consists of three parts: the earpieces, the mobile computing platform, and the real-time speech-enhancement application. It can run complex algorithms without carrying out heavy calculations on the processors in the hearing aid. Thus, the binaural algorithm is utilized without being limited by complexity and power consumption to improve the user experience. Moreover, the speech-enhancement algorithm can be updated much more easily than in traditional built-in digital signal process hearing aids. A good level of user experience is achieved by combining the hearing aid and mobile computing platform with a 400-MHz transceiver; furthermore, the 400-MHz transceiver can reduce path loss around the body. The concept verification process showed that the overall usage of the central processing unit in the smartphone is around 16%, the signal-to-noise ratios show at least a 30% improvement in some environments, and the whole system delay is 8.8 ms. The presented objective and subjective results show significant improvements regarding user experience and usability brought about by the proposed structure.

Forest fires pose a significant threat to public safety and the environment, and harmful pollutants spread rapidly in areas covered by vegetation. Early detection is very important for preventing forest fires from evolving into catastrophic fires. The traditional prediction methods have relatively low accuracy. They can only identify fires clearly after they occur, making it difficult to meet the requirements of precise real-time detection. The YOLOv5-PSG model proposed in this paper improves the YOLOv5 model. After 300 rounds of training, the average recognition accuracy rate of mAP can reach 93.1%, and the accuracy rate can reach approximately 0.802. After 300 rounds of training and learning, the confidence level can reach about 0.965. This improvement makes fire early warning and prediction more comprehensive and effective, ultimately protecting human life and the environment by mitigating the impact of wildfires.