If love listening to music, over time, you might have started to THINK that I can recognize a hit song. There are some songs that just hit you and you know this is a winner. Given that music is probably the oldest form of entertainment, I am quite sure there are a few people out there who can spot ‘a hit’ music. Sometimes I think I might be one such person.
In fact, this spoting of hit song is so hot right now that computers are also trying it out. The craze is currently a new ‘science’ in machine learning called ‘Hit Song Science’ that tries to predict whether a song will be a hit or a flop based on such things as energy, tempo, danceability, loudness and other higher-level features such as harmonic simplicity (how simple the chord sequence is) etc. There is even a formula out for a hit song. There are, however, those who believe that this is not yet a science. And that there are a few things such as year, musical culture age etc which have a marked influence on popularity of music, that cannot not be gleaned from features in music.
A lot of that discussion is bound to continue, but I believe we can help the machines to predict how popular music can be by telling them how we see popular music. Sort of like training the machines. One of the ways we do this is by asking loads of whether they like some music or not. You are already doing this everytime you watch an online video/youtube. If someone is listening, like I am currently, we can have tones of decisions/intelligence from swams of people ( swam intelligence) to tell the machines how popular music looks like.
The way it would works is, everytime you watch a youtube video, you like it, or dislike it, you are telling the world your biases. The most basic metric for rating video or song popularity is the number of views (or listens). Its logical to assume that a video that has the highest number of views is equally popular.
However, so that the popularity score is estimated in the present, it is important to add a decay function so that as the video ages (length of time since published), you penalize old views more than newer views (old views are ranked lower than newer views).
This allows you to track the popularity of a video over time.
Is a web app that allows you to track the popularity of youtube videos. Youtube has become, sort of, a defacto distribution channel for all artists. When you release a track, everyone rushes to youtube to listen to it.
The Y-Rater app tracks the video viewership over time and applies the decay function to give a score of the ‘hotest’ videos people are currently watching. The app uses the score to rank the videos (you can read more about how the scoring works here ) . At the moment, most of the videos are by Kenyan artists, though the app can work with any video on youtube.
It is still in development, so if you do not find a video you want to track, just suggest it and I will add it to the tracking list.
The data collected in this way can then be used in combination with features of music to determine what flops and hits look like.