Search Results for "recommendation"
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Showing 3 open source projects for "recommendation"
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Paessler: Easy to Use With Enterprise Power. Free TrialYou shouldn't have to choose between a monitoring tool that's easy to use and one that's powerful enough for a complex environment. PRTG's low-code interface lets any admin build dashboards, set alerts and monitor devices without scripting, while custom sensors and full API access are there when your team needs deeper control. One platform, no compromise. Download a free 30-day trial now.
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Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
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NetEase-MusicBox
NetEase cloud music command line version
The high-quality command line version of NetEase Cloud Music is simple, elegant, silky and smooth, and is written based on Python. 320kbps high-quality music. Song, artist, album search. NetEase 22 song charts. Netease new disc recommendation. NetEase Featured Playlist. NetEase Anchor Radio. Private playlist, recommended daily. DJing, local collection, add at any time. Play progress and play mode display. Now playing and desktop lyrics display. Song comment display. One-click to enter the song album. Vimer-style shortcut keys make the operation silky smooth. Numerical shortcut keys can be used. ... -
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SoulSync
Automated Music Discovery and Collection Manager
...The system continuously monitors selected artists and detects new releases, then generates personalized playlists such as Release Radar and Discovery Weekly using its built-in recommendation logic. It can automatically download missing tracks from multiple sources including Soulseek, YouTube, and Beatport, then verify file accuracy through AcoustID fingerprinting to ensure the correct audio was obtained. The platform also enriches tracks with lyrics, album art, and MusicBrainz metadata while organizing files into clean, customizable folder structures. ... -
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RateIt is a GUI tool for performing subjective testing of audio samples based on the MUltiple Stimuli with Hidden Reference and Anchor (MUSHRA) methodology as specified in ITU-R recommendation BS.1534-1.
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