Detection of common events and scenes from audio is useful for extracting and understanding human contexts in daily life.
This paper addresses the problem of infant cry detection in real-world settings.
Over the years, activity sensing and recognition has been shown to play a key enabling role in a wide range of applications, from sustainability and human-computer interaction to health care.
The pervasiveness of mobile cameras has resulted in a dramatic increase in food photos, which are pictures reflecting what people eat.
We collected a dataset of 40, 103 egocentric images over a 6 month period with 19 activity classes and demonstrate the benefit of state-of-the-art deep learning techniques for learning and predicting daily activities.