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A tartalmat a Andres Diaz biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Andres Diaz vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.
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Automatic Mastering: Professional Sound with AI

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Manage episode 507794493 series 3653891
A tartalmat a Andres Diaz biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Andres Diaz vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.
Summary: The episode explains Automatic Mastering with AI, describing how deep learning models analyze a track to adjust dynamics, equalization, and limiting so it sounds professional across platforms like Spotify, Apple Podcasts, and YouTube. It covers what automatic mastering is, how it works, and which tools currently deliver strong results, plus practical steps to start using them for music, podcasts, or other audio projects. Recent updates (2024–2025) improve transient preservation, reduce distortion, add platform- and voice-focused presets, and offer more transparent controls and templates for live workflows. The guide provides actionable tips for achieving vocal clarity, consistent loudness, and a professional sound without fatigue, including workflow steps, voice-specific tweaks, multi-version comparisons, and testing on different playback systems. It emphasizes that AI mastering speeds up production but doesn’t replace the human ear, and it invites experimentation, comparison of tools, and feedback. - What automatic mastering is: AI-driven processing (dynamic range, EQ, limiters) to deliver a ready-to-listen master. - How it works: perceptual math using neural networks trained on thousands of tracks to optimize sound for various platforms. - 2024–2025 updates: better transient handling, less high/mid distortion, podcast/music/voice presets, transparent controls, and live/work templates. - Practical workflow: define sonic goals, choose a reliable AI tool, prep tracks (no clipping, -3 dB peaks), apply appropriate presets, tweak gently, export with platform specs, and test across devices. - Podcast and music tips: prioritize intelligibility, reduce breath and sibilance, adjust high-frequency presence, and use multiple versions to compare. - Final guidance: use AI to speed up workflow but reserve time for final manual checks; consider a cohesion mode for albums or playlists to maintain consistency. - Call to action: share experiences, compare tools in future episodes, and reach out with questions. Remeber you can contact me at [email protected]
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18 epizódok

Artwork
iconMegosztás
 
Manage episode 507794493 series 3653891
A tartalmat a Andres Diaz biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Andres Diaz vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.
Summary: The episode explains Automatic Mastering with AI, describing how deep learning models analyze a track to adjust dynamics, equalization, and limiting so it sounds professional across platforms like Spotify, Apple Podcasts, and YouTube. It covers what automatic mastering is, how it works, and which tools currently deliver strong results, plus practical steps to start using them for music, podcasts, or other audio projects. Recent updates (2024–2025) improve transient preservation, reduce distortion, add platform- and voice-focused presets, and offer more transparent controls and templates for live workflows. The guide provides actionable tips for achieving vocal clarity, consistent loudness, and a professional sound without fatigue, including workflow steps, voice-specific tweaks, multi-version comparisons, and testing on different playback systems. It emphasizes that AI mastering speeds up production but doesn’t replace the human ear, and it invites experimentation, comparison of tools, and feedback. - What automatic mastering is: AI-driven processing (dynamic range, EQ, limiters) to deliver a ready-to-listen master. - How it works: perceptual math using neural networks trained on thousands of tracks to optimize sound for various platforms. - 2024–2025 updates: better transient handling, less high/mid distortion, podcast/music/voice presets, transparent controls, and live/work templates. - Practical workflow: define sonic goals, choose a reliable AI tool, prep tracks (no clipping, -3 dB peaks), apply appropriate presets, tweak gently, export with platform specs, and test across devices. - Podcast and music tips: prioritize intelligibility, reduce breath and sibilance, adjust high-frequency presence, and use multiple versions to compare. - Final guidance: use AI to speed up workflow but reserve time for final manual checks; consider a cohesion mode for albums or playlists to maintain consistency. - Call to action: share experiences, compare tools in future episodes, and reach out with questions. Remeber you can contact me at [email protected]
  continue reading

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