I was looking for a home automation project to select and play specific music album from stream services. There are similar ideas of using NFC tags. Basically, it means preparing some NFC tags with album/movie cover arts on them. And putting a tag on the reader will trigger the playback of that album/movie. While it brings the joy of handling and selecting physical collections, it costs money and time to prepare those NFC tags and I wanted to avoid that.
Since now we have those machine learning models and classifiers, I thought I can just train up a model to look at a webcam photo of a record / CD and tell me the Spotify link to play that album.
BTW, I know Microsoft co-pilot (or maybe OpenAI too) can do it without any special training, but then I don't want to pay extra for that and just wanted to host the model on my own machines.
I imagine it will be something like this:
I put an album in front of a webcam...
... and the model will tell me the Spotify URL to pass on to the music streamer
Long story short, my model can a identify my music collection with a 98% correctness (more on that later). If you are interested in the technical details and the scripts used to train the model, they are available on github: https://github.com/kitsook/AlbumSpotter
But eventually I didn't integrate this into my home automation, which is kind of related to the correctness. When I got a new CD / vinyl record, I always add that to my collection on Spotify. So I can just get the cover arts from Spotify to train my model. But then I discovered there are at least two problems that will affect the correctness:
- there are many editions of the same album. e.g. I could have a physical CD of the standard edition but my Spotify collection has the extended edition with a different song list
- nowadays artists tend to release an album with several "special" cover arts. My physical copy could look totally different from the one on Spotify
That means I will need to cleanup the data for a more accurate result. As procrastination kicks in, I am stopping the project with just the machine learning model and the home automation part will be a future project.
