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@@ -94,7 +94,7 @@ The specification includes an [example](https://www.w3.org/TR/webnn/#examples) t
 
 ## Key scenarios
 
-There are many important [application use cases](https://www.w3.org/TR/webnn/#usecases-application) for high-performance neural network inference. One such use case is deep-learning noise suppression (DNS) in web-based video conferencing. The following sample shows how the [NSNet2](https://github.com/microsoft/DNS-Challenge/tree/master/NSNet2-baseline) baseline implementation of deep learning-based noise suppression model may be implemented using the WebNN API.
+There are many important [application use cases](https://www.w3.org/TR/webnn/#usecases-application) for high-performance neural network inference. One such use case is deep-learning noise suppression (DNS) in web-based video conferencing. The following sample shows how the [NSNet2](https://github.com/microsoft/DNS-Challenge/tree/a052ad5a7714bcc6069b515666e837bf973099de/NSNet2-baseline) baseline implementation of deep learning-based noise suppression model may be implemented using the WebNN API.
 
 ```JavaScript
 // Noise Suppression Net 2 (NSNet2) Baseline Model for Deep Noise Suppression Challenge (DNS) 2021.