From my previous blogposts, I’ve explained that most models involving music of some kind use spectrograms as their vehicle of comparison. CNNs and RNNS are a popular choice for these algorithms for a couple reasons, the most obvious being that they both run on images. Aside from their aptitude for image inputs, CNNs and RNNs are best at pattern matching. This is due to their ability to link each pixel to its surrounding in multiple ways: CNNs because of its kernel convolutions and RNNs because of its memory of previous inputs/calculations.

First, the most salient feature of a CNN (which…


Google’s Magenta Project

Aside from Google’s Magenta project, Music and AI isn’t a field that’s been highly explored (especially any field other than music generation, which is much easier than music analysis in my opinion). From my own experience in AI and my experience as a classical musician, I have some insights.

First, coding with music is HARD. It’s not something you can teach yourself in one night, as I’ve unfortunately learned over the past couple weeks. …


For a long time, music and AI were considered separate, opposites on the spectrum of human creation. How could such a benchmark of human creativity, originality, and humanity be translated into 1s and 0s and analyzed by a computer? How could a computer compose music, something that takes humans years to master? Because of this cognitive dichotomy, music and AI have begun to intersect only in the last few years. …


My name is Ally, and I’m a sophomore at the Lakeside School in Seattle, Washington! I play the violin, participate in competitive math contests, and, when it’s not raining, play both indoor and beach volleyball. But, I’ve always loved coding: the freedom to construct nearly anything, projects sometimes consuming me for hours at a time. From there, it was natural that I became hooked in the AI field.


Three months ago, for the first time in nearly half a year, I conducted a perusal of my junk mail inbox. Nothing new popped out, nothing caught the eye. Until I stumbled upon an email addressed to me. ‘Hey,’ I thought. The sender’s name was legitimate, and the accuracy of my name was uncanny. It didn’t seem like any old scammer. Tapping open the message, I scanned through a quick greeting from a random woman (I later discovered it was my sister’s college counselor) and the introduction to the main topic — a Stanford AI camp. …

ally b

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