KINERA URD Review: Ranked #1007 of 1369

Last updated: October 17, 2026

The KINERA URD ranks #1007 out of 1369 total In-Ear Headphones for sound quality. See all Headphones rankings for every top pick.

Brand: Kinera

Prices on this page were last checked October 17, 2026 at 6:04 PM UTC. Product prices and availability are accurate as of that date/time and are subject to change; the price and availability shown on Amazon at the time of purchase will apply to the purchase of this product.

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#1007 KINERA URD: $650.00 Kinera $650.00 eBay

Pros: A published AutoEQ preset corrects its response toward the Harman target, so a few minutes of EQ setup closes most of the gap.

Cons: Treble about 4 dB below target from 3 to 10 kHz, which can sound dull or veiled next to brighter headphones. An uneven response, 2.6 dB standard deviation from target.

Is the KINERA URD right for you?

Buy it if

  • you are sensitive to bright or sharp treble (treble sits 3.9 dB below target).
  • you want sub-bass rumble that reaches down to 20 Hz.
  • you are happy to use EQ: our preset lifts its Harman match by about 27 points.

Skip it if

  • you want the best value: the higher-rated EPZ G10 costs about $626 less.
BackClosed
ConnectionWired
Deviation From Target2.56 dB standard deviation
Driver TypeTribrid
Drivers2 DD + 1 BA + 2 EST (2 x 8mm coaxial dynamic drivers, 1 x Kinera customized 11021 balanced armature, 2 x Sonion EST65DB01 electrostatic drivers)
Form FactorIn-ear
Frequency Response5Hz - 50kHz
Harman Preference Score64 (median of 8 squig.link measurements, calibrated to AutoEQ)
Impedance20 ohms
Sensitivity107 dB
Sub-bass ExtensionWithin 6 dB of target down to 20 Hz (+1.1 dB at 30 Hz)
Tonal Tilt-1.03 (negative is warmer, positive brighter)
Weight0 lbs (5.5 g) (per earbud)

EQ Presets

Harman target

Set a preamp of -6.0 dB, then add these filters in any parametric equalizer (Wavelet, Equalizer APO, Peace, or a player with parametric EQ). Calculated by Speaker Ranking from the median of 8 squig.link measurements, in the filter layout AutoEQ uses. Predicted Harman match with this EQ: 91 out of 100, up from 64.

FilterFrequencyGainQ
Low shelf105 Hz-1.5 dB0.70
High shelf10,000 Hz+1.5 dB0.70
Peak227 Hz-2.5 dB0.82
Peak2,066 Hz-1.1 dB2.47
Peak3,354 Hz+6.0 dB1.36
Peak5,916 Hz+3.3 dB6.00

Neutral target

Set a preamp of -6.0 dB, then add these filters in any parametric equalizer (Wavelet, Equalizer APO, Peace, or a player with parametric EQ). Calculated by Speaker Ranking from the median of 8 squig.link measurements, in the filter layout AutoEQ uses. Predicted Neutral match with this EQ: 92 out of 100, up from 26.

FilterFrequencyGainQ
Low shelf105 Hz-10.0 dB0.70
High shelf10,000 Hz+1.5 dB0.70
Peak23 Hz-1.3 dB2.37
Peak212 Hz-2.3 dB0.63
Peak2,066 Hz-1.1 dB2.61
Peak3,354 Hz+6.0 dB1.36
Peak5,916 Hz+3.3 dB6.00

Studio flat target

Set a preamp of -6.3 dB, then add these filters in any parametric equalizer (Wavelet, Equalizer APO, Peace, or a player with parametric EQ). Calculated by Speaker Ranking from the median of 8 squig.link measurements, in the filter layout AutoEQ uses. Predicted Studio flat match with this EQ: 87 out of 100, up from 6.

FilterFrequencyGainQ
Low shelf105 Hz-11.7 dB0.70
High shelf10,000 Hz+2.0 dB0.70
Peak196 Hz-4.1 dB0.60
Peak1,820 Hz-0.9 dB2.17
Peak2,954 Hz+6.0 dB1.31
Peak3,834 Hz+1.4 dB6.00
Peak4,996 Hz-1.3 dB6.00
Peak6,481 Hz+1.2 dB0.94

Warm target

Set a preamp of -6.0 dB, then add these filters in any parametric equalizer (Wavelet, Equalizer APO, Peace, or a player with parametric EQ). Calculated by Speaker Ranking from the median of 8 squig.link measurements, in the filter layout AutoEQ uses. Predicted Warm match with this EQ: 91 out of 100, up from 75.

FilterFrequencyGainQ
Low shelf105 Hz-1.2 dB0.70
High shelf10,000 Hz-1.5 dB0.70
Peak217 Hz-2.3 dB0.94
Peak3,434 Hz+5.9 dB2.24
Peak6,188 Hz+2.4 dB6.00

Bass target

Set a preamp of -6.0 dB, then add these filters in any parametric equalizer (Wavelet, Equalizer APO, Peace, or a player with parametric EQ). Calculated by Speaker Ranking from the median of 8 squig.link measurements, in the filter layout AutoEQ uses. Predicted Bass match with this EQ: 92 out of 100, up from 70.

FilterFrequencyGainQ
Low shelf105 Hz+2.3 dB0.70
High shelf10,000 Hz+1.5 dB0.70
Peak231 Hz-2.5 dB0.87
Peak2,066 Hz-1.1 dB2.44
Peak3,354 Hz+6.0 dB1.36
Peak5,917 Hz+3.3 dB6.00

Bright target

Set a preamp of -6.2 dB, then add these filters in any parametric equalizer (Wavelet, Equalizer APO, Peace, or a player with parametric EQ). Calculated by Speaker Ranking from the median of 8 squig.link measurements, in the filter layout AutoEQ uses. Predicted Bright match with this EQ: 83 out of 100, up from 14.

FilterFrequencyGainQ
Low shelf105 Hz-10.0 dB0.70
High shelf10,000 Hz+2.0 dB0.70
Peak219 Hz-2.2 dB0.52
Peak1,939 Hz-1.7 dB2.07
Peak3,354 Hz+6.0 dB1.04
Peak5,506 Hz+3.5 dB3.77

V-shaped target

Set a preamp of -6.8 dB, then add these filters in any parametric equalizer (Wavelet, Equalizer APO, Peace, or a player with parametric EQ). Calculated by Speaker Ranking from the median of 8 squig.link measurements, in the filter layout AutoEQ uses. Predicted V-shaped match with this EQ: 79 out of 100, up from 59.

FilterFrequencyGainQ
Low shelf105 Hz+3.0 dB0.70
High shelf10,000 Hz+2.0 dB0.70
Peak52 Hz+0.8 dB2.06
Peak243 Hz-1.1 dB1.15
Peak1,150 Hz-0.9 dB3.22
Peak1,939 Hz-1.7 dB1.08
Peak3,148 Hz+6.0 dB0.99
Peak3,894 Hz+0.9 dB2.10
Peak5,377 Hz+3.9 dB3.34

Frequency Response

How the KINERA URD measures across the audible range, against the Harman in-ear target that most listeners prefer in blind tests. Where the line sits above the target that range sounds louder (more bass, brighter treble), below it quieter. The curve is the median of 8 independent measurements, so one odd sample or rig cannot skew it, and both lines are shifted so their 500 Hz to 2 kHz average sits at 0 dB.

KINERA URDHarman target
Measured frequency response of the KINERA URD-24-18-12-60+6+12+18dB20501002005001k2k5k10k20k

Measurement data: median of 8 measurements from the squig.link databases of adri-n, hbb, hu-fi, ianfann, listener, musicafe, practiphile and zerostresslevel. Thanks to each of them for publishing their work.

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