SANE2018 | Justin Salamon - Robust Sound Event Detection in Acoustic Sensor Networks скачать в хорошем качестве

SANE2018 | Justin Salamon - Robust Sound Event Detection in Acoustic Sensor Networks

Justin Salamon, Senior Research Scientist at New York University’s Music and Audio Research Laboratory and Center for Urban Science and Progress, presents his work on sound event detection in acoustic sensor networks at the Speech and Audio in the Northeast (SANE) workshop, Google, Cambridge, October 18, 2018. More info on the SANE workshop series: http://www.saneworkshop.org/ Abstract: The combination of remote acoustic sensors with automatic sound recognition represents a powerful emerging technology for studying both natural and urban environments. At NYU we've been working on two projects whose aim is to develop and leverage this technology: the Sounds of New York City (SONYC) project is using acoustic sensors to understand noise patterns across NYC to improve noise mitigation efforts, and the BirdVox project is using them for the purpose of tracking bird migration patterns in collaboration with the Cornell Lab of Ornithology. Acoustic sensors present both unique opportunities and unique challenges when it comes to developing machine listening algorithms for automatic sound event detection: they facilitate the collection of large quantities of audio data, but the data are unlabeled, constraining our ability to leverage supervised machine learning algorithms. Training generalizable models becomes particularly challenging when training data come from a limited set of sensor locations (and times), and yet our models must generalize to unseen natural and urban environments with unknown and sometimes surprising confounding factors. In this talk I will present our work towards tackling these challenges along several different lines with neural network architectures, including novel pooling layers that allow us to better leverage weakly labeled training data, self-supervised audio embeddings that allow us to train high-accuracy models with a limited amount of labeled data, and context-adaptive networks that improve the robustness of our models to heterogenous acoustic environments.