@@ -49,7 +49,18 @@ The key issue of this approach is to find the correct number of musical sections
* calinski-harabaz: maximizes the calinski-harabaz index
* davies-bouldin: maximizes the davies-bouldin index
### Differential transform
The differential transform aims at computing spectral differences between two consecutive frames and going back to temporal waveform.
Modifiable parameters are located in the same params.py as for spectral clustering.
The key point is that frames can be beat synchronous or onset synchronous, thus all frames have not the same duration.
Usage:
```
python3 TF_differential.py audio/filename.wav
```
From an original idea of Jean-Marc Chouvel (http://www.ems-network.org/spip.php?article294).
### Bibliography
* B. McFee and D P.W. Ellis (2014). Analyzing song structure with spectral clustering. In proc. of International Society for Music Information Retrieval.
