[HN Gopher] Automatically classifying the content of sound files...
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       Automatically classifying the content of sound files using ML
        
       Author : msephton
       Score  : 43 points
       Date   : 2023-08-13 17:11 UTC (1 days ago)
        
 (HTM) web link (blog.gingerbeardman.com)
 (TXT) w3m dump (blog.gingerbeardman.com)
        
       | mnky9800n wrote:
       | This was a bit disappointing because I was hoping this would be
       | an unsupervised learning example. This is not the author's fault.
       | However there is a large need for unsupervised acoustic event
       | detection and classification. Current methods like lta/sta, match
       | filter, etc. Are not good enough anymore. I will be happy to see
       | this kind of technology grow or be pointed in the right
       | direction.
        
         | jononor wrote:
         | Can you provide some more details on your use case? In general
         | a powerful approach is to use a neural network to extract
         | vector embeddings and then do simple models on top. The neural
         | network could for example be a pretrained audio classifier
         | (like YAMNet mentioned in article), or a model trained with
         | self-supervised learning on representative (unlabeled) data. A
         | small model for unsupervised event detection / segmentation
         | could be agglomerative clustering. A data-efficient supervised
         | approach could be K Nearest Neighbors.
        
           | mnky9800n wrote:
           | As an example, I have 9 months of hydrophone cable data that
           | has a large number of sounds that occur across a large
           | variety of time lengths and frequencies. It would be nice to
           | have a method that highlights certain signals given specific
           | meta data. Tbh, what I think I want is a pipe dream.
        
             | gopher_space wrote:
             | I think some of the folks working with cetaceans might be
             | able to point you towards interesting areas. I'm aware of
             | work on processing petabytes of highest-res hydrophone data
             | and I'm pretty sure they're tagging things that look like
             | new clicktrains in some manner. Does this sound
             | appropriate?
             | 
             | As an aside, I've been kicking around the thought of what a
             | software resonator might look like and where the analogy
             | would work. I feel like there's a _really_ efficient filter
             | you could pull out of the concept.
        
             | jononor wrote:
             | If you are willing to label 100 instances of each event
             | type, then the approaches suggested above should work. And
             | half of the data should be used to quantify how well it
             | works.
        
             | akomtu wrote:
             | What kind of metadata?
        
         | msephton wrote:
         | Sorry, can you explain what you were hoping for? Do you want to
         | be able to classify sounds in a live audio feed? That should be
         | possible relatively easy, by recording a feed and then every so
         | often cutting off the most recent chunk for processing. If I've
         | misunderstood, please explain further.
        
           | ladberg wrote:
           | I think they were hoping that the off-the-shelf model the
           | author used was a novel technique trained on unlabeled data,
           | not a dataset labeled by humans.
        
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