Emma Ozanich

TL;DR: This work surveys the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics, and highlights ML developments in four acoustICS research areas: source localization in speech processing, source localized in ocean acoustic, bioacoustics and environmental sounds in everyday scenes.

TL;DR: In this paper, the authors survey the recent advances and transformative potential of machine learning (ML) including deep learning, in the field of acoustics and highlight ML developments in four acoustICS research areas: source localization in speech processing, source localization from ocean acoustic, bioacoustics, and environmental sounds in everyday scenes.

TL;DR: Recordings of three different ships of opportunity on a vertical array were used as training and test data for the feed-forward neural network and support vector machine classifiers, demonstrating the feasibility of machine learning methods to locate unseen sources.

TL;DR: A nonlinear deep feed-forward neural network (FNN) for direction-of-arrival (DOA) estimation and the practicality of the deep FNN model is demonstrated on Swellex96 experimental data for multiple source DOA on a horizontal acoustic array.

TL;DR: In this article, a deep learning approach based on big data is proposed to locate broadband acoustic sources using a single hydrophone in ocean waveguides with uncertain bottom parameters, where several 50-layer residual neural networks are used to handle the bottom uncertainty in source localization.