Cluster-based noise gate

The noise gates of PodCleaner is a noise gate which analyzes clusters of speech and not the individual samples, allowing you to produce a cleaner audio track, more natural and less “dirty” due to possible clicks and background noises.
Unlike the traditional noise gates, which are not by their nature predictive PodCleaner analyze all files, identifying those that are clusters in which there is sound and then analyzing each individual cluster based on the duration and the RMS power.
And if a cluster is “insignificant” then it is interpreted as a background noise and “cut off”.
As you can see in this picture the cluster-based noise gate is more powerful than a traditional noise gate: it just more “spike” noises:

But there’s more: the attack of cluster-based noise gate is softer and helps to dissolve to the spoken part in a more natural way.
Even the tail of speech is most sweet:

Auto synchronization

PodCleaner uses an intelligent algorithm that takes advantage of pauses in speech to synchronize the separate audio tracks recorded by each participant to the podcast, each on their computer.
The synchronized traces can be saved separately or mixed down into a single audio file.
And, of course, each track is first cleaned individually with PodCleaner filters.