I’m part of a small group of Jr Self Taught Web Developers who were recently brainstorming ideas for a Group Project App we could put together and actually create a user base.
I offered up the suggestion of a podcast application which would have the major feature of being akin to YouTube Sponsor Block, but specifically for podcast episodes.
Essentially, a user contributed database of timestamps for podcast episodes where the mention of cutting to sponsored ads or mentions of sponsorships would be marked so they could be edited out of the episode and then the user could also download said episode where ads are cut out of the final audio file.
My idea was shot down due to fears of possibly infringing on copyright and we ended up with going with another idea. I’m certainly not upset, and am actually excited with the project idea we did choose, but it did get me wondering about whether this idea actually could have legal implications.
I know specifically with YouTube there appears to be a sort of legal loophole that prevents Google from suing projects like invidious, yt-dlp, and YouTube Sponsor Block, but am unaware of the specific details as to how this works.
Thusly, I just wanted to ask if anyone has any insights into whether this project idea would incur any legal infractions from the likes of IheartRadio and other media platforms?
To be clear, I’m not seeking legal advice here, and I’ll be taking any responses with a grain of salt, but I just wanted to see if anyone knows anything on this subject and the legal concerns raised.
I very much dislike being advertised to and podcasts are one of the last bastions of media where advertisements still come up regularly and I’d love to make this application for those who are frustrated with how often they have to skip through sponsor mentions.
Thanks in advance.
From what I understand, they’re able to practically make custom audio files for every download. Sharing the time stamps wouldn’t work that well. Re-distributing podcasts without the ads would definitely land you in legal trouble, cause every audio file is their “work of art”.
Not a problem for ublock because you’re editing their work of art for your personal use, and sharing unaltered stuff.
And youtube sponsor block is just sharing time stamps you might be interested in.
AI system that can recognize patterns and auto skip forward?
Maybe if you could distribute audio files (or hashes of audio files) that mark the start and stop of ads, that would solve the problem.
I guess podcasters could combat this by inserting random noise into their audio files, but they probably wouldn’t do that.
They don’t need to add random noise, they just do what they already do and insert new advertising materials. Your static timestamps mean the ads and content end up at different locations.
I’m not suggesting static timestamps, but small audio files of the podcast about to enter, and just exited an ad.
The app could then search for the clips in the podcast to get the timestamp.
If there are copyright issues of sharing small clips, you can just save a hash of a clip, which will allow the app to find a match, but is not itself the Intelecual property of the podcaster; The hash cannot be turned back into the audio file. The hash would be smaller than the audio clip anyway, so sharing hashes would be better
Some perceptural hash of the actual ads could work to. You could run into legal trouble sending the ads themselves or the hosts speaking.
Good idea! I bet you could make good ad library by comparing the audio between episodes of the same podcast (to catch the ads read by the host) and between different podcasts (to catch the targeted ads inserted into a lot of podcasts)
Such a library of ads would quickly become massive, even if stored in a series of hash references. Interesting idea though.
That’s an intriguing approach. Saving a hash of the clip instead of the timestamps MIGHT work. I’m still a bit worried about legal ramifications in that case.
Yeah, that’s a problem. Dynamic length of targeted advertisement breaks would mean even a user generated database of timestamps wouldn’t be that useful.
Yes, using a trained AI model that recognizes ad segments could be possible for this, albeit expensive due to the cost of GPUs on a VPS.