add google doc link

app.py

@@ -167,7 +167,7 @@ Thomas Radinger [ thomasrad@protonmail.com | [LinkedIn](https://www.linkedin.com
 
 Birds are key indicators of ecosystem health and play pivotal roles in maintaining biodiversity [1]. To monitor and protect bird species, automatic bird sound recognition systems are essential. These systems can help in identifying bird species, monitoring their populations, and understanding their behavior. However, building such systems is challenging due to the diversity of bird sounds, complex acoustic interference and limited labeled data. 
 
-To tackle these challenges, we expored the potential of deep learning models for bird sound recognition. In our work, we developed two Audio Spectrogram Transformer (AST) based models: BirdAST and BirdAST_Seq, to predict bird species from audio recordings. We evaluated the models on a dataset of 728 bird species and achieved promising results.  Details of the models and evaluation results are provided in the table below. As the field-recordings may contain various types of audio rather than only bird songs/calls, we also employed an Audio Masked AutoEncoder (AudioMAE) model to pre-classify audio clips into bird, insects, rain, environmental noise, and other types [2].
+To tackle these challenges, we expored the potential of deep learning models for bird sound recognition. In our work, we developed two Audio Spectrogram Transformer (AST) based models: BirdAST and BirdAST_Seq, to predict bird species from audio recordings. We evaluated the models on a dataset of 728 bird species and achieved promising results.  Details of the models and evaluation results are provided in the table below. As the field-recordings may contain various types of audio rather than only bird songs/calls, we also employed an Audio Masked AutoEncoder (AudioMAE) model to pre-classify audio clips into bird, insects, rain, environmental noise, and other types [2]. For a full report on work workflow and results, please refer to [link](https://docs.google.com/document/d/17uRGEVz4hxShK4fvWQzIKFJlVwEg9p1rAT9XXDYGE3w/edit?usp=sharing).
 
 Our contributions have shown the potential of deep learning models for bird sound recognition. We hope that our work can contribute to the development of automatic bird sound recognition systems and help in monitoring and protecting bird species.