This paper presents a Speech Enhancement (SE) technique based on multi-objective learning convolutional neural network to improve the overall quality of speech perceived by Hearing Aid (HA) users. The proposed method is implemented on a smartphone as an application that performs real-time SE. This arrangement works as an assistive tool to HA. A multi-objective learning architecture including primary and secondary features uses a mapping-based convolutional neural network (CNN) model to remove noise from a noisy speech spectrum. The algorithm is computationally fast and has a low processing delay which enables it to operate seamlessly on a smartphone. The steps and the detailed analysis of real-time implementation are discussed. The proposed method is compared with existing conventional and neural network-based SE techniques through speech quality and intelligibility metrics in various noisy speech conditions. The key contribution of this paper includes the realization of CNN SE model on a smartphone processor that works seamlessly with HA. The experimental results demonstrate significant improvements over the state-of-the-art techniques and reflect the usability of the developed SE application in noisy environments. INDEX TERMS Convolutional neural network (CNN), speech enhancement (SE), hearing aid (HA), smartphone, real-time implementation, log power spectra (LPS).

In this letter, we derive a new super Gaussian Joint Maximum a Posteriori (SGJMAP) based single microphone speech enhancement gain function. The developed Speech Enhancement method is implemented on a smartphone, and this arrangement functions as an assistive device to hearing aids. We introduce a “tradeoff” parameter in the derived gain function that allows the smartphone user to customize their listening preference, by controlling the amount of noise suppression and speech distortion in real-time based on their level of hearing comfort perceived in noisy real world acoustic environment. Objective quality and intelligibility measures show the effectiveness of the proposed method in comparison to benchmark techniques considered in this paper. Subjective results reflect the usefulness of the developed Speech Enhancement application in real-world noisy conditions at signal to noise ratio levels of -5 dB, 0 dB and 5 dB.

In this paper, we present an asthma risk prediction tool based on machine learning (ML). The entire tool is implemented on a smartphone as a mobile-health (m-health) application using the resources of Internet-of-Things (IoT). Peak Expiratory Flow Rates (PEFR) are commonly measured using external instruments such as peak flow meters and are well known asthama risk predictors. In this work, we find a correlation between the particulate matter (PM) found indoors and the outside weather with the PEFR. The PEFR results are classified into three categories such as 'Green' (Safe), 'Yellow' (Moderate Risk) and 'Red' (High Risk) conditions in comparison to the best peak flow value obtained by each individual. Convolutional neural network (CNN) architecture is used to map the relationship between the indoor PM and weather data to the PEFR values. The proposed method is compared with the state-of-the-art deep neural network (DNN) based techniques in terms of the root mean square and mean absolute error accuracy measures. These performance measures are better for the proposed method than other methods discussed in the literature. The entire setup is implemented on a smartphone as an app. An IoT system including a Raspberry Pi is used to collect the input data. This assistive tool can be a cost-effective tool for predicting the risk of asthma attacks.

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