On Fri, 5 Aug 2005 23:52:04 -0600 Robert Johnston <[EMAIL PROTECTED]> wrote:
... > Anyways, How about we take the principals of Neural Networks or > Bayesian Filtering and apply it to Myth Commercial Flagging. ... > We can then (optionally) take these "Learned Rules" and send them > (anonymously) back to a central database, so every myth box > contributes and the whole Myth community acts as a very big (and very > intelligent) Distributed Neural Net. ... > be done once, by the developer of the system, and his "Base Data" can > be used as a starting-point for ad-detection on all other systems. I'm following, but I'm not sure you've stated what data is used to create these rules. Based on your scenario where 'user hit rewind after auto-skip', are you suggesting time as a basis? AFA brainstorming, I submit that the majority of commercials have a higher words-per-minute ratio than most programs (News or daytime talk-shows might be an exception). Perhaps http://aubio.piem.org/ or something similar, could be used to map out a rough words-per-minute count. Pitch detection is another idea, odds are that the voices/sounds in commercials are dissimilar to the program being recorded. Perhaps pitch-variation is a better term for what I'm suggesting. Similarly, like blank frames detection, blank frames + audio-silence seems logical. Regarding a 'central database' for X, that's twice in two days now -- see: http://www.gossamer-threads.com/lists/mythtv/users/143525 BTW - I'm quite satisfied with the current state of commercial flagging, don't get me wrong. If only there were a max_commercial_length setting, I'd manually intervene far less often. I haven't used the 'aubio' package, maybe I'll test my WPM theory on some actual recordings. -Jay p.s. I'd send this to mythtv-brainstorming, but this thread is already here. Apologies, if you consider it noise.
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