{author: hhr, date: 191216, function: comment}
Poz:
I also enjoyed the "lurking" feeling of reading other parallel conversations
:) The same here. This is of course the difference with cooperate platforms that monitor what you read - here we never see what you read where and when… it only becomes apparent in the writing.
{author: hhr, date: 191217, kind: note, keywords: [sweep, impulse response, space, interaction]}
Probing the space. What if I sent out tiny sweep burst to measure impulse responses, deliberately picking up all the smearing from interactions in the space?
Following the experiments with statistical filtering, I added arithmetic mean and spectral flatness features to the filter calculation, which can be used as additional selectors, passing through only certain chunks. I implemented a Histogram UGen for FScape (this will go into the next release version of Mellite), to understand the dynamic of these features. I find it interesting that histograms are basically counting mechanisms, so I can see a relation to Ji's ideas about counting steps.
{author: hhr, date: 200111, function: contextual, keywords: [statistical, filter, counting, histogram]}
I experimented with feeding the fragments through the minimum phase segmentation, but this wasn't good yet, as I need better range information for the segmentation. Instead, I now feed it through a strong limiter to flatten the loudness differences between the fragments, followed by a "paul stretch" time stretch; currently with fixed factor 8, but this should be determined by the signal itself. Also it should probably allow overlapping segments.
What I like very much about this is that there is a simple control now over the density of events. Instead of basing the thresholds on one fixed histogram measurement, probably that measurement should also be done at intervals by the system.
{author: hhr, date: 200111, kind: caption, keywords: [filter, minimum phase, segmentation, stretch, density]}
Histogram of arithmetic mean of spectral frames of the filter function obtained from a microphone recording. There are 256 bins which where taken from the log-arithmetic mean in the range of -10 to 0.0. The filter function is (A - B) / B, where A = long term median window of spectral magnitudes, B = short term median window of spectral magnitudes.
{author: hhr, date: 200111, kind: caption, keywords: [statistical, filter, counting, histogram]}
Histogram of geometric mean of the same signal. There are 256 bins which where taken from the log-geometric mean in the range of -50 to 0.0.
{author: hhr, date: 200111, kind: caption, keywords: [statistical, filter, counting, histogram]}
Histogram of spectral flatness (geometric mean divided by arithmetic mean) of the same signal. There are 256 bins which where taken from the log-geometric mean in the range of -50 to 0.0.
{author: hhr, date: 200111, kind: caption, keywords: [statistical, filter, counting, histogram]}
Corresponding Mellite workspace
{kind: repository}
