What would it look like if machines can see music?
Machines can learn human behaviors and contents. With Strong AI, machines can recognize images, voices, text, sound etc. But, as we all know, machine interpretation is different from human understanding. Especially in music, human understand and express music in abstract ways, but machines understand music in more analytic and data driven ways. Moreover, with AI, machines can understand music on higher level, which means beyond time dimension. We listen music as time series, but machines can analysis and re-organized it upon its sonic feature distinction. Therefore, my question is, what would music look like with machine’ perspective?
This project, “What if machines can see music…?” visualizes similarities and relationships of many audio chunks from a single audio track. From this visualization, we can see how many audio events happened in one audio file ( via number of chunks) and how those chunks are related to each other (via clustering). And also, you can listen those chunks as time series. Every audio file has different sonic events and feature distinctions. Therefore, with this visualization, each music file can have its own from and figure.
Can machines be creative? Yes, I guess, because machines can see the world not like human does and never like human can image. This is machine singularity which human doesn’t have.
1. As input, a single audio track (a song) into many audio chunks.
2. Those chunks happened according to the beginning of discrete sonic events in the input audio.
3. With librosa lib( mel-spectrogram), features are extracted from those audio chunks.
4. as result, 26 features from each chunk.
5. clustering those chunks via t-sne according to feature similarities
1. Visualizing high dimensional data in low dimensional space. 26 features of audio chunks in 3D space with t-sne.
2. Two tracks of visualization
2-1) first track : as time order, all the chunks are shown at the bottom. The size of each box represents the length of the audio chunk.
2-2) second track : place the audio chunks in 3D space according to its feature similarities with other chunks.
3. Sound : play each chunks according to its time order.
4. As result, every audio file can have its own form and shape.
Images and Videos Courtesy of Sey Min