update doc

app.py

@@ -50,7 +50,7 @@ with open("article.md") as f:
 
 interface_options = {
     "title": "Urban Sound 8K Classification",
-    "description": "Fast AI example of using a pre=trained ResNet34 vision model for an audio classification task on the [Urban Sounds](https://urbansounddataset.weebly.com/urbansound8k.html) dataset. ",
+    "description": "Fast AI example of using a pre-trained Resnet34 vision model for an audio classification task on the [Urban Sounds](https://urbansounddataset.weebly.com/urbansound8k.html) dataset. ",
     #"article": article,
     "interpretation": "default",
     "layout": "horizontal",

article.md

@@ -1,15 +1,39 @@
+> Note: The examples provides may not work on Safari, tablets and iOS devices. Try an alternate approach. 
 
-Dataset for this - https://urbansounddataset.weebly.com/urbansound8k.html
+## Dataset 
 
-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 
+- [UrbanSound8K](https://urbansounddataset.weebly.com/urbansound8k.html)
 
+## Audio files 
 
+Files are converted to melspectrograms that perform better in general for visual transformations of such audio files. 
 
-#Fast.ai was used to train this classifier with a Resnet34 vision learner with 3 epochs.  Audio files converted to Mel Spectrograms that perform better in general for visual transformations of such audio files.
+## Training 
+
+Using With Fast.ai and three epochs with minimal lines of code approaches 95% accuracy with a 20% validation of the entire dataset of 8732 labelled sound excerpts of 10 classes shown above. Fast.ai was used to train this classifier with a Resnet34 vision learner with three epochs.
 
 epoch	train_loss	valid_loss	accuracy	time
 0	1.462791	0.710250	0.775487	01:12
+
 epoch	train_loss	valid_loss	accuracy	time
 0	0.600056	0.309964	0.892325	00:40
 1	0.260431	0.200901	0.945017	00:39
-2	0.090158	0.164748	0.950745	00:40
+2	0.090158	0.164748	0.950745	00:40
+
+# 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)
+ 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)
+
+
+## Credits 
+
+Thanks to [Kurian Benoy](https://kurianbenoy.com/) and countless others that generously leave code public.