Persistent system audio logger that identifies tracks in real time
Captures system audio in real time and produces a chronological timeline of identified tracks, running without installation. monad, built by leduftw for Windows desktops, watches whatever your PC plays and uses the AudD recognition service to name items automatically. The app targets music discoverers and content creators who want passive logging, offering portable execution and native support for both x64 and ARM64 Windows devices.
What does the app listen for and record?
The tool captures system audio output, identifies tracks via the AudD API, and writes entries to a chronological timeline. It monitors audio produced by media players, browsers, and other applications, then submits identification queries and logs the returned metadata. The developer supplies portable builds, so you can run the program without modifying the system registry, and the project is open source under the MIT License.
Does it slow down a machine during continuous monitoring?
Background monitoring is designed to remain lightweight, with the project described as portable and low-overhead rather than a full installation. The app supports both x64 and ARM64 architectures, which lets it run natively on modern devices. Because it operates as a persistent listener, expect continuous background activity and occasional network use for identification requests; the source material frames the design as unobtrusive rather than resource-heavy.
Is it safe to run and how much technical setup is required?
Open-source code and a portable mode reduce trust barriers, since the repository can be audited and no installer changes the system. Identification depends on the AudD service, so the app issues external queries and requires network access for that function. Once the listener starts, the timeline is created automatically with no ongoing manual input, which makes daily operation simple for nontechnical users after initial launch.
Who should run this tool and what trade-offs to expect
The app fits listeners and creators who accept automatic, networked identification in exchange for passive logging. It is most useful when you don't want to intervene during playback, but identification depends on an external recognition service, so offline use is limited. If you accept that trade-off and keep network access available, the tool serves its niche well. Recommended.
