On a similar note Matt Parker recently released a video about Perlin Noise, which won an Oscar (for Technical Achievement) in 1996: https://www.youtube.com/watch?v=JrLSfSh43oA
Another anecdotal description of old staircases that I've heard of before is from Burgos castle in Spain, where (it's said) that the stairs to the bottom of the well change direction half way down to prevent you from getting too dizzy [1]
> Se accede al interior por unas escaleras de caracol. Para evitar el mareo, los 4 primeros tramos se hacen en el sentido de las agujas del reloj y los dos últimos tramos en sentido contrario.
A similar embeddings model based on Discogs genre/style data is the Effnet-Discogs model made at the Music Technology Group at Universitat Pompeu Fabra: https://replicate.com/mtg/effnet-discogs
In fact, ListenBrainz (partner project to MusicBrainz) is doing some stuff similar to what you mention about listening histories. We're using the data to generate similarity based on when songs are listened to each other in "listening sessions" along with other songs.
Hi, Freesound developer here, this is great! We're a small research team at a university[1], and we're also working on auto-captioning tasks that include trying to describe sound effects based on the audio content, although we're not doing anything with OpenAI or summarisation of metadata at the moment.
A reminder, we also have acoustic similarity using signal processing analysis and nearest neighbour lookup for all sounds[2]
Could you update the sound displays to include a link to the sound page on Freesound, for citation purposes? You can use https://freesound.org/s/[soundid] or use our embedded player. See the "Embed" option in the share box on the right -hand-side toolbar of any sound page.
Are you interested in integrating this into FS? Talk to us![3]
I encountered something similar to this last month. I was visiting Canada from Spain, and got an esim from https://www.airalo.com/. Due to what I guess is airalo obtaining transit from the cheapest provider, the data connection ended up being bounced through Czechia.
This meant that my android phone was a bit confused at times. When on mobile data the weather widget would default to showing me weather for random Czech cities, and search results would be Czech-localised. When on wifi connections, results were in English and Canada-localised, unless I bounced through a VPN exit node at home.
After 3-4 days, ads on Youtube started becoming Canada or Czechia-localised depending on if I was watching it via wifi or mobile data. It seems that google eventually decided that I was in Canada, continuing to play me Canadian ads even when I got home to Spain, on both wifi and mobile data. What was even stranger is that my partner started getting Canadian ads on youtube on her ipad too, which doesn't have any details about my google account (other than us living on the same internet connection). It took about 2 weeks for google to start playing us Spanish ads again.
The tickets are definitely pitched as "for regular commuters", but I'm going to take advantage of them for getting further out of Barcelona to cycle. I've spent 70€ on 4 tickets and expect that I'll receive refunds for 2 of them (by travelling at least 16 times on a ticket). A return ticket to a location 2 hours away ends up being close to 15€, so even if I get no refunds due to not travelling enough, it's still economical to buy the tickets if I'm planning on using them 2-3 times a month.
Not a ton of experience biking in the region, but I rode from Barcelona to Montserrat and it is quite lovely. There are a few sections where you're on a road busier with cars than one would like, but I've found Spanish drivers to be fairly respectful of bikers*. It was about 3.5h there, 2.5h back, with a nice 600m climb up the mountain at the end of the trip. The views up there are simply stunning.
* Fun story, I rode 1500 miles throughout Spain ~10 years ago, and had not a single bad experience with a motorist. When I got back to my home in NY state, literally on my first bike ride back, within 2 miles of my starting point, a man in a truck threw a beer can at me.
Such things are very regional yeah. I live in Spain and there drivers are respectful because cyclists have nowhere else to go.
In the Netherlands where I'm from we frown on cyclists on the road because there's excellent cycle lanes they should be using. The problem is the "sporty" types that think they're too good for those and use the main road instead.
Whoa! Freesound looks like it might be one of the last few missing pieces I need for a long term passion project.... (I love, and occasionally am lucky enough to be paid to work on, audio signal processing things.)
* goes to create an acct *
Hah! Apparently I made an account 9 years ago, excited to see if it's useful to me now. :D
Do you happen to do audio cleanup? There was a java app 5 years or more ago that could clean up old recordings of records. It worked quite well, and I was looking at paying for it when lightning put that PC out of commission, so I don't have browser history either. Had I actually paid I'd have an email.
I used it to clean up some old police training videos with narration by Jack Webb on YouTube. Didn't tag the video with the software, something I normally do.
The software was better than Adobe and audacity. I'd like to find it because I want to 'fix' the audio on quite a few Old Time Radio Researchers titles.
I love this project idea! I don't want to oversell myself, I "do things" not nearly as much as I'd like or should (or used to).... more like "I study, research, fool around with equations, tend to know a lot about, &c". ;-)
But yeah, noise removal is in there! Other forms of restoration too.
_Ideally_ if you can somehow remember what the program you used was, it's possible for me to poke and see which approach they're using. But, assuming that knowledge is lost to the wind, can you link me to the recordings? Ideally a few "before and after". That way I can load them into MATLAB and start to see what's up.
Adobe's Audition is pretty good, iZotope RX is also well loved.
EDIT: Either way, and even if I don't end up doing anything with it, I'd love a link to the recordings. Dirty and clean.
I used that Java app to clean up about a dozen of my vinyl records! It was the best for transient pops.
For general grunge removal, that's best done by cleaning the record as best you can; heavy post processing tends to screw it up. Learning Izotope is on my list of projects.
Java app. Real simple. Now I need to recall what it was... but for time frame, it was around 2005.
Not that you asked but, for transient clicks and pops, a Compressed Sensing (badass new (past 2 decades) applied mathematics) based approach I expect would far outperform anything previous. (Previous approaches used higher order statistics, linear prediction, median cut"ish" things, filtering IIRC.)
I wonder if any of the commercial shops have made CS based impulse noise removal based plugins. I wonder if anyone would pay $$$ for it.
This is actually still a difficult task and still under active research. The name of the phenomenon is "tempo octave error". Typically an algorithm looks for evenly-spaced strong pulses of energy, and infers the BPM from that. If there is a strong beat at multiple of the actual BPM (half, double, 4x, etc) then it could be mistakenly identified as the BPM. As alin23 points out in a sibling comment it seems like the Spotify algorithm at least has a confidence level here. There is some more information about BPM computation and octave errors at https://www.audiolabs-erlangen.de/resources/MIR/FMP/C6/C6S2_...
For key and BPM data, the MetaBrainz foundation also has AcousticBrainz: https://acousticbrainz.org/ (I'm a developer on this project).
Unfortunately, I would say that the data that we have in AcousticBrainz isn't as good as what's in the Spotify API, although the algorithms that we use are completely free (available in the Essentia signal processing library
- https://essentia.upf.edu/). Over the last few years the algorithms in Essentia have improved and we're hoping to release a new version of the tool used in AcousticBrainz to improve the database.
