| Website | https://aleksati.net |
New Year's is always when I maintain my music tech blog, updating style, dependencies and fixing bugs. Always a pain. Maintenance is the true killer of DIY projects.
In 2025, I've written 4 posts on everything from compression methods for neural audio effects to my new screensaver app with real-time images of the Sun. Fun to look back on 🥸
If you're interested in this (see the slideshow for more), check out the posts through the link in my bio. Many of them have cool audio+code examples!
Here's a short presentation about where the synth.is project stands and where it's headed next:
So far there is a prototype for running #sound discovery simulations with #evolutionary #algorithms, and a #web interface for exploring the results. The next step is to figure out how this could fit into #music production workflows. And perhaps most importantly, what is a good way for us humans to interact with and guide the evolutionary process?
Last year, I led some workshops about advanced audio networking in OSC, focused on using timestamps to synchronize musical devices.
In a new post, I try to refine my workshops into a practical, step-by-step guide to developing your own synchronization systems with OSC, using a technique called Forward Synchronization between Python and Pure Data.
Read more here - https://aleksati.net/posts/osc-timestamps-and-forward-synchronization
Have a nice weekend~
Last year, I led several workshops on audio networking with OSC, focusing on using timestamps to synchronize musical devices. In this post, I refine my workshops into a practical, step-by-step guide to developing your own synchronization scheme with OSC, using a technique called Forward Synchronization between Python and Pure Data.
Measuring latency in networked performances might seem fairly straightforward, but the reality is far from it. Lately, I've been writing about latency in these scenarios, specifically about this gap between the science of measuring and the reality of doing it in the field.
Strategies for Measuring Audio Latency in Networked Art Performances - https://aleksati.net/posts/strategies-for-measuring-audio-latency-in-nmp
Strategies for Measuring Video Latency in Networked Art Performances - https://aleksati.net/posts/strategies-for-measuring-video-latency-in-nmp
📷 by T. Rajendran
Improve your studio monitor placements! This technique is well-known to all you spatial audio people out there, but maybe less known amongst more "normal" audio engineers and producers.
Read more - https://aleksati.net/posts/how-to-position-your-studio-monitors-better-and-more-accurately
#studio #monitors #placement #audio #music #technology #acoustics #musicproduction
Improving the positioning of your studio monitors can significantly improve the sound quality of your studio. In this post, I share an easy and practical way to find optimal placements for your studio monitors with just some basic tools and a little know-how.
Interested in gathering lots of audio latency data? Audio Latency Examiner(ALEX) is small software tool I made for batch collecting audio latency data from both hardware and software musical devices, in real-time. All in one. Built with Max8.
Read more: https://aleksati.net/works/audio-latency-examiner
Download: https://github.com/aleksati/audio-latency-examiner
#audio #latency #software #max8 #batchdata #bigdata #musictechnology #programming
New post about how to measure audio latency in networked music scenarios. This is a complicated procedure that involves many moving parts, usually more than you might think, and often on a tight schedule. In the post, I share some good strategies and practical ways to do this in real-life scenarios.
Link - https://aleksati.net/posts/how-to-measure-audio-latency-in-networked-music-systems
#networkedmusic #latency #musictechnology #telematicmusic #audio #networkedmusicperformance
Measuring the audio latency of networked music systems is a complicated procedue where you have to navigate complex technical setups with many moving parts, usually on a tight schedule. In this post, I write about solid strategies and share good practical ways to measure audio latency in networked music contexts.
Recently, I collaborated on the research and software development of DGMD, a software tool for creating datasets from musical devices (such as VSTs and stuff) for various audio-related machine-learning tasks.
Read more - https://aleksati.net/works/dataset-generator-for-musical-devices
The software is available for free for both OSX and Win. Download it here - https://github.com/stefanofasciani/DGMD/releases
#audio #machinelearning #dataset #research #software #ai #vst #musictechnology #smc #programming
Recently, I was involved in a research project aiming to create software to generate datasets from musical devices for audio-related machine-learning tasks. This post takes a closer look at the Dataset Generator for Musical Devices (DGMD), including links to in-depth tutorials and download pages. Original post by Stefano Fasciani.
synth.is
Jenny (inactive archive)