add intro

article.md

@@ -20,20 +20,24 @@ epoch	train_loss	valid_loss	accuracy	time
 1	0.260431	0.200901	0.945017	00:39
 2	0.090158	0.164748	0.950745	00:40
 
-# Classical Approaches
+## Classical Approaches
 
 [Classical approaches on this dataset as of 2019](https://www.researchgate.net/publication/335862311_Evaluation_of_Classical_Machine_Learning_Techniques_towards_Urban_Sound_Recognition_on_Embedded_Systems)
 
 ## State of the Art Approaches 
 
-The state-of-the-art methods for audio classification approach this problem as an image classification task. For such image classification problems from audio samples, three common(https://scottmduda.medium.com/urban-environmental-audio-classification-using-mel-spectrograms-706ee6f8dcc1)
+The state-of-the-art methods for audio classification approach this problem as an image classification task. For such image classification problems from audio samples, [three common] (https://scottmduda.medium.com/urban-environmental-audio-classification-using-mel-spectrograms-706ee6f8dcc1)
  transformation approaches are:
 
-Linear Spectrograms
-Log Spectrograms
-[Mel Spectrograms](https://towardsdatascience.com/audio-deep-learning-made-simple-part-2-why-mel-spectrograms-perform-better-aad889a93505)
+- Linear Spectrograms
+- Log Spectrograms
+- [Mel Spectrograms](https://towardsdatascience.com/audio-deep-learning-made-simple-part-2-why-mel-spectrograms-perform-better-aad889a93505)
 
 
 ## Credits 
 
 Thanks to [Kurian Benoy](https://kurianbenoy.com/) and countless others that generously leave code public. 
+
+## Code Repo & Blog
+
+Additional details on my [Github Repo] (https://github.com/gputrain/fastai2-coursework/tree/main/HW) and [my blog](https://www.gputrain.com/) where I will add additional details on model build, audio transforms and more.