# Estimating Novelty

Synopsis: We can estimate the local novelty in a signal by looking at the accumulated difference of some point in time and its neighbours (argh)

Assumptions: That the measure of similarity is meaningful for the data at hand, that he neighbourhood size bears some resemblances to the time scale of interest

# Computing Similarity

- We're often dealing with multidimensional data
- Choice of ways of estimating similarity, different trade offs (which are?)
- Can use different features (e.g. STFT magnitudes or MFCCs)

# Summarising Novelty

- Reduce matrix to a single running estimate
- Slide a window (the kernel) over the main diagonal of similarity matrix
- Multiply kernel by what it covers and sum
- Peak picking (link)