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Creators/Authors contains: "Coban, Enis Berk"

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  1. ; ; (, NeurIPS Audio Imagination Workshop)
    Accepted Manuscript Full Text Available
  2. ; ; ; (, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics)
    Arctic boreal forests are warming at a rate 2–3 times faster than the global average. It is important to understand the effects of this warming on the activities of animals that migrate to these environments annually to reproduce. Acoustic sensors can monitor a wide area relatively cheaply, producing large amounts of data that need to be automatically analyzed. In such scenarios, only a small proportion of the recorded data can be labeled by hand, thus we explore two methods for utilizing labels more efficiently: self-supervised learning using wav2vec 2.0 and data valuation using k-nearest neighbors approximations to compute Shapley values. We confirm that data augmentation and global temporal pooling improve performance by more than 30%, demonstrate for the first time the utility of Shapley data valuation for audio classification, and find that our wav2vec 2.0 model trained from scratch does not improve performance. 
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    Full Text Available 
  3. ; ; ; (, IEEE Conference on Audio Speech and Signal Processing)
    Sound provides a valuable tool for long-term monitoring of sensitive animal habitats at a spatial scale larger than camera traps or field observations, while also providing more details than satellite imagery. Currently, the ability to collect such recordings outstrips the ability to analyze them manually, necessitating the development of automatic analysis methods. While several datasets and models of large corpora of video soundtracks have recently been released, it is not clear to what extent these models will generalize to environmental recordings and the scientific questions of interest in analyzing them. This paper investigates this generalization in several ways and finds that models themselves display limited performance, however, their intermediate representations can be used to train successful models on small sets of labeled data. 
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    Full Text Available