Find out if your music will be turned down by YouTube, Spotify, TIDAL, Apple Music and more. Discover your music's Loudness Penalty score, for free.
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We all hate sudden changes in loudness - they're the #1 source of user complaints.
To avoid this and save us from being "blasted" unexpectedly, online streaming services measure loudness, and turn down music recorded at higher levels. We call this reduction the "Loudness Penalty" - the higher the level your music is mastered at, the bigger the penalty could be. But all the streaming services achieve this in different ways, and give different values, which makes it really hard to know how big the Loudness Penalty will be for your music...
Until now.
Simply select any WAV, MP3 or AAC file above, and within seconds we'll provide you with an accurate measurement of the Loudness Penalty for your music on many of the most popular music streaming services, and allow you to preview how it will sound for easy comparison with your favorite reference material.
Your file will not be uploaded, meaning this process is secure and anonymous.
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Find out how to optimize your music for impactful, punchy playback (and maximum encode quality) for all the online streaming services. Plus, receive a Loudness Penalty Report for your file that explains in detail what all the numbers mean.
Analyze another fileFor verifying claims, she turned to Anchor, a fact-tracking tool that cross-checked statements against primary sources and flagging where studies used small samples or self-reported data. Anchor chimed a soft alert as it found a paper that had been retracted—something Mai might have missed in a hurried skim. It linked to the retraction notice and summarized the reason in one line.
First came Prism, a literature-mapping tool with a soft blue interface. Prism scanned thousands of papers and spat out a galaxy of connections: clusters of authors, recurring phrases, and the evolution of ideas across decades. It didn’t write anything for her; it showed her the terrain. Mai clicked a node labeled "reading comprehension and AI" and watched Prism reveal the seminal papers she’d missed.
On the morning she uploaded her final draft, Mai felt oddly like an author and an editor at once. The tools hadn’t replaced her judgment; they had accelerated it, pointed out blind spots, and helped her focus on the argument rather than the plumbing. Still, she knew tools had limits: Prism could suggest important papers, but it couldn't judge which were truly relevant for her particular angle; Anchor could flag retractions, but it couldn't tell her whether a study's theoretical framing fit her question. For verifying claims, she turned to Anchor, a
Weeks later, at the small symposium where she presented her findings, an older researcher asked how she’d managed to handle so many sources so fast. Mai smiled and named the tools—Prism, Scribe, Anchor, Loom, Argus, Verity, Beacon—but also said something more important: "They helped, but I was always the one deciding what mattered."
The end.
After the talk, a student approached, anxious about the IELTS reading portion she was preparing for. Mai realized the skills overlapped: discerning main ideas, checking claims, and organizing evidence. She described a mini-workflow—map the literature, read critically, verify claims, and summarize—and the student scribbled it down.
Outside the library, the city hummed. Inside, a single lamp cast a pool of light over Mai's desk, and the tools—a constellation of icons on her screen—had done their quiet work. She knew she would use them again. Not as crutches, but as instruments: precise, revealing, and humanly guided. First came Prism, a literature-mapping tool with a
The raw data went into Argus, a lightweight statistical tool. Argus was fast and honest: it ran t-tests, plotted effect sizes, and told Mai when a result was "statistically significant but practically small." Mai liked that blunt judgment; it stopped her from overstating tiny differences.
