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+---
+date: 2024-12-18 9:00:00-7:00:00
+description: null
+featuredImage: assets/acoustic_species_id/2024_12_18_NeurIPS_Poster.jpg
+author: Sean Perry and Ludwig von Schoenfeldt
+layout: blog-post
+slug: /acoustics-at-neurips-2024
+title: Acoustic Species ID goes to NeurIPS 2024!
+categories:
+- news-and-updates
+tags:
+- acoustic-species-id
+- bioacoustics
+---
+
+Our work, "A Deep Learning Approach to the Automated Segmentation of Bird Vocalizations from Weakly Labeled Crowd-sourced Audio" was accepted and presented at NeurIPS 2024 in the "Tackling Climate Change with Machine Learning" Workshop hosted by Climate Change AI! Congrats to the authors: Jacob Ayers, Sean Perry, Samantha Prestrelski, Tianqi Zhang, Ludwig von Schoenfeldt, Mugen Blue, Gabriel Steinberg, Mathias Tobler, Ian Ingram, Curt Schurgers and Ryan Kastner.
+
+{% include 
+    img_caption.html
+    src="assets/acoustic_species_id/2024_12_18_NeurIPS_Canda_Place.jpg"
+    caption="View of Canada Place, an iconic landmark of Vancouver. View taken from the east side of the Vancouver Convention Center. The conference actually took place in both buildings with an underground tunnel connecting the two, as seen on the bottom right of the image. Taken by Sean Perry"
+%}
+
+This focused on the issue of weakly labeled datasets, often associated with large, bioacoustic crowdsourced  datasets. Traditional methods frequently use approaches rooted in digital signal processing approaches to identify the species of interest. The paper takes a look at testing these methods with deep learning models. It can be found [here](https://www.climatechange.ai/papers/neurips2024/8). Credits to **Mathias Tobler** for first conceptualizing the idea.
+
+Key contributions to this work include [PyHa](https://github.com/UCSD-E4E/pyha), the Python repository where the main technologies used in the paper are stored. Credits primarily to **Jacob Ayers** for creating the repo and for his vision of the project and **Samantha Prestrelski** for developing it and carrying out experiments. Further thanks to **Gabriel Steinberg** for his technical contributions with isolation techniques and chunking methods and **Mugen Blue** for his training of TweetyNet, which was the most successful method used (as seen in the paper). 
+
+Shout out to **Sean Perry** for developing [Pyrenote](https://github.com/UCSD-E4E/Pyrenote), the tool used to label the data used in the project. 
+
+Last week, **Sean Perry** and **Ludwig von Schoenfeldt** attended NeurIPS 2024 and presented the work! The two traveled out of the country to Vancouver, Canada to attend most of the conference, getting to see hundreds of posters, amazing research in machine learning, and present their own work! It was an inspiring moment getting to see where the future of the field could be heading.
+
+Acoustic Species is planning many exciting extensions to this work. We will continue to evaluate how these methods may influence the behavior of upstream models as we continue to work to improve bioacoustic machine learning techinques to identify species of interest. 
+
+{% include 
+    img_caption.html
+    src="assets/acoustic_species_id/2024_12_18_NeurIPS_North_Vancover.jpg"
+    caption="View of North Vancouver, taken from the west side of Canada Place looking northwest. The previous day was raining and the storm had started to move on north,  appearing over the valleys of the mountains and the ski resorts. Taken by Sean Perry" 
+%}
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