There is a simple music recommender webapp shown in the video. From your model you got a python function that maps one song (e.g. by artist,title) to other songs. What is the fastest way to build this interactive webapp (for internal, experimental) use?
Building a Music Recommender with Deep Learning
31–40 of 74 posts
Re: Building a Music Recommender with Deep Learning
#32This is interesting. My first thought was to wonder how a LSTM would do. Once might think it would be a better representation for music? There's some models which use convolutional layers along with a LSTM for video representation (eg [1]) and it would be interesting to see if convolutions are useful for capturing similar themes of music. I wonder if one could build a music embedding (word2vec style) and use similari…
I could be totally off on this, but his encoding is an image and LSTM is for time series, which would require a different representation.
I completely agree LSTM would be useful as it would by default require a different representation. I think most commenters agree this representation is overly simplistic. Amazed it works as well as it does!
Re: Building a Music Recommender with Deep Learning
#33Interesting. You didn't specify, I'm guessing you did 3x3 convolutions on the spectrographs? Also, how did you choose the convolution size, number of conv/pooling layers, etc? Did you consider asymmetric convolution/pooling layers to account for the differences between the frequency and time dimensions?
There are a number of interesting directions you could go with that data set. One interesting possibility is to make a convolutional autoencoder, then use that to apply "deep dreaming" filters to music. Another interesting evolution would be to handle the frequency dimension using a 1D convolution, and run a RNN on top of that to deal with time.
Re: Building a Music Recommender with Deep Learning
#34Why trying to do it via A.I.? Why not checking which are the top 3 most played songs by other users who are the 1000 users who have the most similarity with the current user, and then recommend the current user the most played songs from the 1000 similar users that the current user has not listened to yet. As far as I can see this would be superior to any existing A.I. recommendation algorithm.
What you're describing is also an "A.I". It's called collaborative filtering, and your algorithm (picking top 3 of the 1000 most similar users) would give results heavily biased towards popular songs, there are better approaches in that field.
Re: Building a Music Recommender with Deep Learning
#35There is a simple music recommender webapp shown in the video. From your model you got a python function that maps one song (e.g. by artist,title) to other songs. What is the fastest way to build this interactive webapp (for internal, experimental) use?
For internal/experimental/exploratory use, I like Jupyter [0].
Re: Building a Music Recommender with Deep Learning
#36There is a simple music recommender webapp shown in the video. From your model you got a python function that maps one song (e.g. by artist,title) to other songs. What is the fastest way to build this interactive webapp (for internal, experimental) use?
For quick interactive data apps, there is also now Dash by Plot.ly[0]. It aims to do for Python what shiny does for R.
Re: Building a Music Recommender with Deep Learning
#37There is a simple music recommender webapp shown in the video. From your model you got a python function that maps one song (e.g. by artist,title) to other songs. What is the fastest way to build this interactive webapp (for internal, experimental) use?
For quick interactive data apps, there is also now Dash by Plot.ly[0]. It aims to do for Python what shiny does for R. [0] https://plot.ly/products/dash/
Thank you very much
Re: Building a Music Recommender with Deep Learning
#38There is a simple music recommender webapp shown in the video. From your model you got a python function that maps one song (e.g. by artist,title) to other songs. What is the fastest way to build this interactive webapp (for internal, experimental) use?
For internal/experimental/exploratory use, I like Jupyter [0]. 0: https://jupyter.org/
I appreciate it I'm gonna try it soon
Re: Building a Music Recommender with Deep Learning
#39Very cool! One minor nitpick -- the author mentions that this is 'completely unsupervised'. It's true that the author didn't need to manually classify the data, but someone did. So, I believe that this is actually supervised learning, as the author is training a classifier on preexisting labels (the genres). I believe that unsupervised learning would not make use of a target variable at all. If the network architectu…
Correct, the CNN classifier is definitely supervised learning.
Re: Building a Music Recommender with Deep Learning
#40This music CNN classifier could be used to match songs that mix (transition) well together, having similar textures.
Within one song: Infinite Gangnam Style | https://news.ycombinator.com/item?id=4709472
Within one arbitrary song (Inifinite Jukebox - no longer working?): http://labs.echonest.com/Uploader/index.html
https://www.reddit.com/r/infinitejukebox/comments/4cmr4f/met...