Show HN: An e-ink frame that hears birds and draws them as 1800s illustrations
231–240 of 266 posts
Re: Show HN: An e-ink frame that hears birds and draws them as 1800s illustrations
#232Re: Show HN: An e-ink frame that hears birds and draws them as 1800s illustrations
#233For those of you that have a Samsung frame tv and no spare e ink screens:
Inspired by AvianVisitors [1] I made a similar project [2] with an android app for bird detection using Perch, and then displays the birds detected during the last 24 hours with nice art on a samsung frame tv in "art mode".
If you dont want to repurpose an old android device, my app also supports bird detections from birdnet-go and birdweather. It should be plug and play with this project.
Have a look at [1] https://theodore.net/projects/AvianVisitors/ [2] https://github.com/simenf/birdframe
Re: Show HN: An e-ink frame that hears birds and draws them as 1800s illustrations
#234Re: Show HN: An e-ink frame that hears birds and draws them as 1800s illustrations
#235Re: Show HN: An e-ink frame that hears birds and draws them as 1800s illustrations
#236Re: Show HN: An e-ink frame that hears birds and draws them as 1800s illustrations
#237The underlying classifier, BirdNET, is a traditional neural network and not an LLM: https://doi.org/10.1016/j.ecoinf.2021.101236
Are there any LLMs being widely used for audio classification? I know VLMs are being used a lot in image stuff. It always seems kind of silly to me to throw everything at an LLM. I know they’re huge and can automatically handle a huge number of tasks but something in me finds it wasteful when we could be creating easily trainable, cheap to run bespoke models for a lot of stuff
Determining which tool to use should be a lightweight operation but I’m not expert enough to understand exactly how much lighter than a full LLM call just to recognize it needs a different tool or model.
Re: Show HN: An e-ink frame that hears birds and draws them as 1800s illustrations
#238Earlier quoted context omitted.
There are LLMs that support audio input, similar to those with vision support. From my testing of open weights LLMs with audio support, they basically are only trained to recognize audio as an alternative to text input, they treat audio as basically equivalent to a transcript, and can't recognize or distinguish things like music, accents, background sounds, etc. So they're only really good for transcribing or summari…
Have you had any luck fine tuning one with musical data for classification or music-aware QA? I've been hacking on https://trebel.la/ which I would like to be a music practice companion, and the biggest missing feature is actually useful audio-based feedback pipeline. My current approach, not yet validated, is trying to generate training data from masterclass recordings on Youtube, and then fine tuning MOSS-audio on…