  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. While many recognition tasks have
traditionally employed inertial sensors, acoustic-based methods offer the
benefit of capturing rich contextual information, which can be useful when
discriminating complex activities. Given the emergence of deep learning
techniques and leveraging new, large-scaled multi-media datasets, this paper
revisits the opportunity of training audio-based classifiers without the
onerous and time-consuming task of annotating audio data. We propose a
framework for audio-based activity recognition that makes use of millions of
embedding features from public online video sound clips. Based on the
combination of oversampling and deep learning approaches, our framework does
not require further feature processing or outliers filtering as in prior work.
We evaluated our approach in the context of Activities of Daily Living (ADL) by
recognizing 15 everyday activities with 14 participants in their own homes,
achieving 64.2% and 83.6% averaged within-subject accuracy in terms of top-1
and top-3 classification respectively. Individual class performance was also
examined in the paper to further study the co-occurrence characteristics of the
activities and the robustness of the framework.

Human-Computer Interaction;Machine Learning;Sound;Audio and Speech Processing