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Context-aware computing, which gathers information about the environment at a given time and adapts its behavior accordingly, can be effectively applied to a monitoring system that automatically detects and copes with emergency situations. In this paper, we propose an indoor emergency awareness alarm system using a deep neural network on a mobile device for at-risk people, such as the elderly and children. The proposed system detects emergency situations and quickly delivers the state of the person being monitored to a guardian using a mobile transmission system. To do this, three types of contextual information, including sound, human activity, and indoor location, are instantly recognized by the protected person’s mobile device. Both sound and human activity are used to improve the recognition accuracy of emergency situations provided to guardians’ mobile devices. Sound events are detected by a residual neural network, and human activities are recognized by applying the accelerometer and gyroscope signals of the mobile device to a deep spiking neural network. In addition, the indoor location where the emergency occurred is detected by the deep spiking neural network. The experimental results show the high accuracy of the indoor emergency awareness framework.

Music listening is a very personal and situational behavior for modern people who always carry smartphones in everyday life. Therefore, contextual information, such as user’s current activity and mood state could be used to greatly improve music recommendations. In this paper, we develop a smartphone-based mobile system that includes two core modules for recognizing human activities and then accordingly recommending music. In the proposed method, a deep residual bidirectional gated recurrent neural network is applied to obtain high activity recognition accuracy from accelerometer signals on the smartphone. In order to improve the performance of tempo-oriented music classification, an ensemble of dynamic classification using a long-term modulation spectrum and sequence classification using a short-term spectrogram is used. Music recommendation is performed using the relationship between the recognized human activities and the music files indexed by tempo-oriented music classification that reflects user preference models in order to achieve high user satisfaction. The results of comprehensive experiments on real data confirm the accuracy of the proposed activity-aware music recommendation framework.

This paper proposes a high performance playout scheduling and packet loss concealment algorithm at the receiver for enhancing Voice over Internet Protocol (VoIP) speech quality. In the proposed method, arriving packets are classified by an adaptive thresholding approach based on the analysis of multiple features of short signal segments. The excellent classification results are used in the playout scheduling and packet loss concealment. In adaptive playout, the buffering time is minimized by way of playing out normally or compressing each packet according to accurate network jitter estimation. Additionally, linear prediction-based packet loss concealment delivers high voice quality by alleviating the metallic artifacts due to concealing consecutive packet loss or recovering lost packets1.

The quality of real-time Voice over Internet Protocol (VoIP) networks is affected by network impairments such as delays, jitters, and packet loss. To solve this issue, this paper proposes a new receiver-based enhancing method of VoIP speech quality. Our approach is based on the combined playout control and signal reconstruction technique that consists of a set of algorithms that conceal packet loss, reduce buffering delay, detect spike delay, and alleviate packet delay jitter. The proposed fully receiver-based enhancing algorithm is computationally efficient, delivers high-quality voice service, and is suitable for use in any practical mobile VoIP system.

Time-shift is a crucial function of interactive televisions as like DVR and Internet TV broadcasting services. Automatic important event detection allows users utilize time- shift function conveniently. In this paper, we propose a method to extract important events in baseball videos. In the proposed method, we first detect play scenes and audio events separately from video and audio tracks. For robust play scene extraction, we proposed off-line learning model having local adaptation based on ongoing analyzed video. And we implemented the audio event detection with a SVM-based classifier. Final important events are determined by a combination of each audio-visual detection results in real time. We evaluated our method with a baseball database of Korean and Major League games. Experimental results show that the implemented system runs in real time and achieves a remarkable performance of 0.85 recall and 0.97 precision rates.