@inproceedings{discovery1503675,
         journal = {UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
           title = {DeepEar: Robust Smartphone Audio Sensing in Unconstrained Acoustic Environments using Deep Learning},
            year = {2015},
       publisher = {Association for Computing Machinery},
           month = {September},
          series = {ACM International Joint Conference on Pervasive and Ubiquitous Computing},
          editor = {H Miyahara and H Tokuda and K Mase and M Langheinrich},
       booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing},
           pages = {283--294},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
{\copyright} 2015 ACM},
         address = {Osaka, Japan},
          volume = {2015},
        keywords = {Mobile sensing, deep learning, audio sensing.},
             url = {http://dx.doi.org/10.1145/2750858.2804262},
        abstract = {Microphones are remarkably powerful sensors of human behavior and context. However, audio sensing is highly susceptible to wild fluctuations in accuracy when used in diverse acoustic environments (such as, bedrooms, vehicles, or cafes), that users encounter on a daily basis. Towards addressing this challenge, we turn to the field of deep learning; an area of machine learning that has radically changed related audio modeling domains like speech recognition. In this paper, we present DeepEar - the first mobile audio sensing framework built from coupled Deep Neural Networks (DNNs) that simultaneously perform common audio sensing tasks. We train DeepEar with a large-scale dataset including unlabeled data from 168 place visits. The resulting learned model, involving 2.3M parameters, enables DeepEar to significantly increase inference robustness to background noise beyond conventional approaches present in mobile devices. Finally, we show DeepEar is feasible for smartphones by building a cloud-free DSP-based prototype that runs continuously, using only 6\% of the smartphone's battery daily.},
          author = {Lane, ND and Georgiev, P and Qendro, L}
}