fix: other typos and minor changes/rewordings

_posts/2024-12-18-NeurIPS-2024-acoustics.md

@@ -21,15 +21,15 @@ Our work, "A Deep Learning Approach to the Automated Segmentation of Bird Vocali
     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 crowdsource dataset. Traditional methods frequently use digital signal processing approches to identify the species of interest. This work takes a look at testing these methods with deep learning models. [The paper can be found here](https://www.climatechange.ai/papers/neurips2024/8). Credits to **Mathias Tobler** for first conceptualizing the idea. 
+This focused on the issue of weakly labeled datasets, often associated with large, crowdsourced bioacoustic datasets. Traditional methods frequently use approaches rooted in digital signal processing 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 techonologies used in the paper are stored. Credits primaily to **Jacob Ayers** for creating the repo and for the vision for the project and **Samantha Prestrelski** for the work she has done developing it and carrying out experiments. Further thanks to **Gabriel Steinberg** for his techical contributions with isolation techinques and chunking methods and **Mugen Blue** for his training TweetyNet, which was the most successful method used as seen in the paper. 
+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 [Pyrenote](https://github.com/UCSD-E4E/Pyrenote) by **Sean Perry** for providing the the labeling for the project. 
+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 be continuing to evalute 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. 
+Acoustic Species is planning many exciting extensions to this work. We will continue to evalute 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