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update doc

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  1. app.py +1 -1
  2. article.md +28 -4
  3. melspectrogram.PNG +0 -0
app.py CHANGED
@@ -50,7 +50,7 @@ with open("article.md") as f:
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  interface_options = {
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  "title": "Urban Sound 8K Classification",
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- "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. ",
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  #"article": article,
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  "interpretation": "default",
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  "layout": "horizontal",
 
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  interface_options = {
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  "title": "Urban Sound 8K Classification",
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+ "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. ",
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  #"article": article,
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  "interpretation": "default",
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  "layout": "horizontal",
article.md CHANGED
@@ -1,15 +1,39 @@
 
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- Dataset for this - https://urbansounddataset.weebly.com/urbansound8k.html
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- 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
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- #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.
 
 
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  epoch train_loss valid_loss accuracy time
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  0 1.462791 0.710250 0.775487 01:12
 
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  epoch train_loss valid_loss accuracy time
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  0 0.600056 0.309964 0.892325 00:40
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  1 0.260431 0.200901 0.945017 00:39
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- 2 0.090158 0.164748 0.950745 00:40
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ > Note: The examples provides may not work on Safari, tablets and iOS devices. Try an alternate approach.
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+ ## Dataset
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+ - [UrbanSound8K](https://urbansounddataset.weebly.com/urbansound8k.html)
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+ ## Audio files
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+ Files are converted to melspectrograms that perform better in general for visual transformations of such audio files.
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+ ## Training
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+
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+ 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.
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  epoch train_loss valid_loss accuracy time
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  0 1.462791 0.710250 0.775487 01:12
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+
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  epoch train_loss valid_loss accuracy time
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  0 0.600056 0.309964 0.892325 00:40
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  1 0.260431 0.200901 0.945017 00:39
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+ 2 0.090158 0.164748 0.950745 00:40
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+
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+ # Classical Approaches
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+
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+ [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)
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+
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+ ## State of the Art Approaches
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+
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+ 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)
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+ transformation approaches are:
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+
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+ Linear Spectrograms
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+ Log Spectrograms
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+ [Mel Spectrograms](https://towardsdatascience.com/audio-deep-learning-made-simple-part-2-why-mel-spectrograms-perform-better-aad889a93505)
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+
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+
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+ ## Credits
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+
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+ Thanks to [Kurian Benoy](https://kurianbenoy.com/) and countless others that generously leave code public.
melspectrogram.PNG ADDED