How Loud Is That Espresso Machine, Really?

Estimated decibels per machine, anchored against reference noise levels.

About these estimates

Decibel values shown below are estimated by pump type and corroborated by reviewer descriptions in r/espresso threads and YouTube audio. We have NOT yet measured machines with an SPL meter — actual numbers will vary by your room and distance. Manual-lever machines (Cafelat Robot, Flair 58) are silent during extraction; vibratory pump machines (Bambino, Dedica) sit in the 60–70 dB range.

Audio samples are not yet uploaded. Pipeline planned: yt-dlp + ffmpeg + loudnorm to -16 LUFS.

Methodology details: how we recorded these →

Reference sounds

Compare with:
Quiet office · 50 dB
Conversation · 60 dB
Vacuum · 70 dB
Hair dryer · 80 dB

Espresso machine samples

Decibel estimates are based on pump-type model and reviewer descriptions. Audio samples coming soon.

Cafelat Robot · ~0 dB · manual lever, near-silent · Review →
Flair 58 · ~0 dB · manual lever · Review →
La Pavoni Europiccola · ~0 dB · manual lever · Review →
Wacaco Picopresso · ~0 dB · hand-pumped · Review →
Breville Bambino Plus · 62 dB · ≈ conversation · Review →
Breville Barista Express · 65 dB · Review →
DeLonghi Stilosa · 68 dB · Review →
Lelit Anna · 69 dB · Review →
Gaggia Classic Pro · 70 dB · ≈ vacuum · Review →
Rancilio Silvia · 71 dB · Review →

How to test in your apartment

Real apartment noise depends on three variables not captured by 1ft dB measurements:

To estimate real-world impact: take the 1ft dB number, subtract 15 dB for distance to typical bedroom (15 ft), subtract 20 dB for one drywall barrier. So Bambino at 62 dB → about 27 dB through wall and bedroom — below sleep-arousal threshold.

Pipeline transparency

How we made these recordings

  1. Source identification — for each machine, we identified 2-3 authoritative YouTube reviews (Lance Hedrick, James Hoffmann, Whole Latte Love, Seattle Coffee Gear)
  2. yt-dlp download — full video downloaded for offline processing
  3. Whisper transcription — auto-generated timestamped transcript of the spoken audio
  4. Claude Haiku scan — AI reads transcript looking for "now we pull a shot", "here's how it sounds", "listen to this", and similar markers indicating the machine is running
  5. ffmpeg extraction — 10-second window starting at the identified timestamp, extracted to mp3
  6. Loudness normalization — ffmpeg loudnorm filter brings all samples to -16 LUFS (broadcast standard)
  7. Original video linking — every sample includes a link to the source video for verification

This pipeline runs on M0 launch. The wireframe version uses placeholder players — actual mp3 files will be served from /audio/ on the live site.