#186: Why LUFS Don't Matter As Much As You Think with Ian Shepherd

Music Production Podcast for DIY Music Producers and Artists | Inside The Mix

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In this episode of Inside The Mix, mastering engineer Ian Shepherd demystifies loudness metrics and debunks common mastering misconceptions while offering practical advice for producers looking to improve their masters without chasing arbitrary targets.

Ian explains the role of LUFS in mastering, why normalization matters, and how focusing on musicality and dynamics leads to better results than simply hitting a loudness target.

What You'll Learn:
• LUFS measurements explained – momentary, short-term, and integrated loudness
• How many LUFS should my master be? Understanding the balance between dynamics and loudness
• The truth about LUFS for Spotify – why 83% of users never change loudness normalization settings
• What does LUFS stand for? And why it's just one piece of the mastering puzzle
• How normalization impacts your music across different streaming platforms
• The role of audio normalization in creating a consistent listening experience
• Why AI mastering struggles to match the emotional intent of human engineers
• Spotify’s approach to loudness and what it means for your masters
• Internal dynamics – how balancing different sections of your song enhances clarity and impact
• The mastering feedback loop – why collaboration between engineers and artists is key

If you want your music to stand out in today’s Spotify-dominated landscape, don’t obsess over loudness numbers. Instead, focus on musicality, dynamics, and emotional impact. Test how your tracks sound at normalized streaming levels, and let the music, not the meters, drive your mastering decisions.

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Chapters

1. #186: Why LUFS Don't Matter As Much As You Think with Ian Shepherd (00:00:00)

2. How loudness perception really works (00:01:09)

3. Introduction to LUFS and loudness measurements (00:04:31)

4. Metering methods and mastering techniques (00:11:29)

5. Loudness normalization in streaming services (00:21:47)

6. The mastering feedback loop (00:36:15)

7. AI mastering limitations (00:45:39)

8. YouTube's stable audio feature explained (00:57:46)

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