Collection of Negative Samples for Hit-Song Prediction
| Thesis Type | Master |
| Thesis Status |
Finished
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| Student | Federica Cenzuales |
| Final |
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| Thesis Supervisor | |
| Contact | |
| Research Field |
Hit song prediction is the task of predicting whether a given song is going to be a hit -- e.g., make it into the charts. One way of realizing this is as a binary classification model which is able to assign a given song to one of two classes: hit or non-hit.
To train such a machine learning model, positive (hits) as well as negative samples (non-hits) are required. Obtaining positive samples is relatively straightforward -- we can define all songs that made it into the charts (e.g., the Billboard Hot 100) as positive samples. Negative samples, on the other hand, are more tricky:
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We have to ensure that the songs we choose as negative samples had the chance to make it into the charts we use as our source for the positive samples.
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For positive samples, there is a natural measure for positivity -- the peak position the song reached on the charts. No such natural measure exists for negative samples.
In this thesis, the goals are to devise a method for collecting true negative samples as well as to develop an effective measure for negativity.