With common parameters, one spectrogram can have a shape of (1290 x 120), i.e. Lower-Dimensional DataĪ 30-second snippet produces quite a large spectrogram. Even if you only have 30-second tracks available, you can get 14 snippets out of each of them with 3-second snippets and 1 second of overlap. In fact, I would argue that this is a form of natural and fairly seamless data augmentation.įor example, you can get more than 80x the training data from a 3-minute track if you draw 3-second snippets with an overlap of 1 second compared to drawing one 30-second snippet per track. Moreover, by allowing for overlap between slices, even more snippets can be drawn. Using divide & conquer, most of the audio signal can be used. Given that you have full-length tracks available, taking a 30-second slice and throwing away the rest of a 3–4 minute track is very data inefficient. However, there are three major advantages to applying the divide & conquer approach: 1. This is partly because commonly used music data sources like the GTZAN or FMA datasets or the Spotify Web API provide tracks of this length.
Often, genre classification is based on exactly one 30-second snippet of a track.
However, other researchers like Nasrullah & Zhao (2019) have also used this method - just not by the same name. In music genre classification, the term was first used (to my knowledge) by Dong (2018). Table 1 - Divide & Conquer in Computer Science and Music Genre Classification.Īlthough the definitions are not the same, they do overlap substantially, and personally, I find the term divide & conquer very fitting in both cases.
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