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Why is this study so underpowered ? It should be trivial to assemble a massive labelled dataset of hits/not-hits. Additionally, every metric should be compared

by screye 3y ago

Why is this study so underpowered ? It should be trivial to assemble a massive labelled dataset of hits/not-hits. Additionally, every metric should be compared to the human baseline, not an ML baseline. If an average nobody can identify a prospective hit-song with higher accuracy than the best ML model, then then the ML baseline is completely meaningless. No offense, but I have seen better ML 101 capstone projects.

art187 3y ago
Because you have to have their device to get the data.
Applejinx 3y ago
I've done this, as just an individual, studying 'evergreen' albums and which ones sustained sales over time. All you have to do is weight the biggest sellers in the platinum-album class and adjust for time on the market (partially compensating in this way for the growing industry artificially pushing albums to be multiplatinum) You get predictable results like Zeppelin, The Eagles, etc. as the 'most hit-laden' records. As far as actually measuring the SOUND with machine learning, you could just take a short-cut and study Mutt Lange's mixes, or you could run the computers on it, but this is not actually an interesting question because everyone's been trying to converge on the 'hit song sound' for decades and decades. You'll get pretty generic and unobjectionable results, and you'll still get blindsided by outliers such as Bohemian Rhapsody (too impossibly long and complicated) and Somebody I Used To Know (very odd arrangement). Targeting the most mass market possible (which is do-able with AI) is such a shotgun approach that it'll tend to lead to failure because too many talented humans have already beat that approach into the ground. What you'd want is for your hit-song AI to 'hallucinate' in an interesting way. You want things a little off-model, a 'hook'. In a conceptual sense, not just a 'mathematically optimal catchy phrase' sense.

by screye 3y ago

art187 3y ago

Because you have to have their device to get the data.

Applejinx 3y ago

I've done this, as just an individual, studying 'evergreen' albums and which ones sustained sales over time. All you have to do is weight the biggest sellers in the platinum-album class and adjust for time on the market (partially compensating in this way for the growing industry artificially pushing albums to be multiplatinum) You get predictable results like Zeppelin, The Eagles, etc. as the 'most hit-laden' records. As far as actually measuring the SOUND with machine learning, you could just take a short-cut and study Mutt Lange's mixes, or you could run the computers on it, but this is not actually an interesting question because everyone's been trying to converge on the 'hit song sound' for decades and decades. You'll get pretty generic and unobjectionable results, and you'll still get blindsided by outliers such as Bohemian Rhapsody (too impossibly long and complicated) and Somebody I Used To Know (very odd arrangement). Targeting the most mass market possible (which is do-able with AI) is such a shotgun approach that it'll tend to lead to failure because too many talented humans have already beat that approach into the ground. What you'd want is for your hit-song AI to 'hallucinate' in an interesting way. You want things a little off-model, a 'hook'. In a conceptual sense, not just a 'mathematically optimal catchy phrase' sense.