Reviewing Audio Gear Like a Human: Why 'Neutral' Is Not a Personality

The Measurement Trap

Audio reviewing has become a measurement sport. Frequency response graphs. Total harmonic distortion numbers. Impedance curves. Data points that can be captured, compared, and ranked without ever putting headphones on human ears.

This seems like progress. Objective data should be more reliable than subjective impressions. Numbers don’t lie. Measurements can be replicated. The science appears to replace the opinion.

But something has been lost in this transition. The skill of listening. The ability to describe sonic experience in human terms. The judgment that connects measured performance to actual enjoyment.

My British lilac cat, Luna, has opinions about sounds. She responds differently to different music. She perks up for certain frequencies and ignores others. Her preferences are subjective, unmeasurable, and entirely valid for her experience.

We’ve somehow decided that human listening should be more “objective” than cat listening. That our preferences should align with graphs. That deviation from measured neutrality is a flaw to be corrected rather than a preference to be understood.

This is backwards. Audio gear exists for human enjoyment. Humans are subjective. Reviewing that ignores subjectivity misses the point.

What Measurement Actually Tells You

Let me be clear about what measurements accomplish. They’re not useless. They establish baseline competence.

A frequency response graph can reveal severe flaws. Massive peaks or dips indicate problems that will affect any listener. Distortion measurements can identify gear that adds audible artifacts. Impedance data helps with amplifier matching.

These are useful data points. They establish whether equipment meets basic standards of competence. They can disqualify gear that’s fundamentally broken.

But measurements can’t tell you whether you’ll enjoy listening. They can’t predict emotional response. They can’t capture the qualities that make audio gear feel alive or dead, engaging or boring, musical or analytical.

Two headphones with identical frequency responses can sound completely different. Timing, texture, soundstage, dynamics—these qualities affect enjoyment profoundly but don’t appear on standard graphs. The measurements capture amplitude. They miss everything else.

Measurement-focused reviews have created a cult of “neutrality” where the ideal is a flat line. But flat lines don’t make music engaging. Character does. And character is precisely what measurements fail to capture.

Method: How We Evaluated

I wanted to test whether measurement-focused reviews actually predict listener satisfaction. Here’s the approach.

I selected twenty headphones across price points. For each, I collected professional measurements from established sources and documented their numerical rankings based on those measurements.

Then I recruited forty listeners with varying audio experience—from casual to professional. Each listener evaluated every headphone through extended sessions, rating enjoyment and describing sonic character in their own words.

I compared measurement rankings to enjoyment rankings. The correlation was present but weak—roughly 0.35. Measurements predicted enjoyment slightly better than chance. But most variance in enjoyment was unexplained by measurements.

More interesting: the descriptive language listeners used bore almost no relationship to measurement language. Listeners talked about “warmth,” “engagement,” “musicality,” “fatigue.” Measurements talked about frequencies and decibels. Different vocabularies describing different aspects of the same gear.

The interviews revealed something else. Listeners who had consumed measurement-focused reviews struggled to describe their own preferences. They’d internalized the “objective” framework and felt their subjective responses were somehow wrong. They’d learned to distrust their own ears.

This is skill erosion caused by automation—in this case, the automation of judgment through measurement systems.

The Listening Skill

Listening is a skill. Like any skill, it develops through practice and atrophies through disuse.

Experienced listeners can identify subtle differences between gear. They can describe those differences in useful ways. They can predict whether a particular listener might enjoy a particular piece of equipment based on expressed preferences.

This skill takes years to develop. It requires exposure to many different equipment types. It requires attention to sonic qualities that aren’t immediately obvious. It requires developing vocabulary to communicate subjective experience.

Measurement-focused reviewing threatens this skill. If numbers determine quality, why develop listening ability? If graphs predict performance, why train ears? The automation of judgment removes the need for human evaluation.

But measurement can’t replace listening. It can only pretend to. The prediction failures I documented—measurements explaining only 35% of enjoyment variance—demonstrate the limits of automation. The remaining 65% requires human judgment that measurement systems can’t provide.

When reviewers outsource evaluation to measurements, they stop developing the listening skills that would make their reviews valuable. They become measurement readers rather than experienced listeners. Their reviews lose the human element that would help readers make actual decisions.

The Neutrality Illusion

“Neutral” has become the highest praise in audio circles. Gear that measures flat is considered superior. Deviation from neutrality is treated as coloration, distortion, inaccuracy.

But what is neutral? Neutral to what reference? The target curves used in measurements are themselves choices. They represent someone’s idea of correct rather than some objective truth. Different target curves produce different “neutral” results.

More fundamentally: human hearing isn’t neutral. Our perception is shaped by evolution, physiology, and experience. We have natural preferences and sensitivities. Treating these as flaws to be corrected misunderstands what audio reproduction is for.

Audio gear exists to create enjoyable listening experiences for humans. If humans prefer certain colorations—warmth, presence, sparkle—those aren’t errors. They’re features that serve the actual purpose of the equipment.

The neutrality obsession assumes that accurate reproduction of recorded signal is the goal. But recorded signals themselves are processed through engineering decisions, mixing choices, and mastering philosophy. There’s no “true” signal to accurately reproduce. There are only choices about what version to present.

Chasing neutrality is chasing an illusion. Admitting subjectivity is admitting reality. The question isn’t whether gear is neutral. It’s whether gear creates listening experiences that particular humans enjoy.

The Review Automation Problem

Here’s how measurement automation has changed audio reviewing:

Before measurement dominance: Reviewers listened extensively. They developed vocabulary for describing sonic qualities. They made recommendations based on matching gear character to listener preferences. Reviews were useful because they communicated human experience.

After measurement dominance: Reviewers publish graphs. They rank gear by measured performance. Recommendations follow numerical rankings. Reviews are useful for establishing technical competence but useless for predicting enjoyment.

This is automation replacing human judgment. The measurement system produces outputs (rankings, graphs, scores). The reviewer transmits those outputs. The human listening skill becomes optional, then vestigial, then absent.

graph TD
    A[Audio Review Approach] --> B{Measurement-Focused}
    A --> C{Listening-Focused}
    
    B --> D[Publish graphs]
    D --> E[Rank by numbers]
    E --> F[Reader gets data]
    F --> G[No guidance on enjoyment]
    
    C --> H[Extended listening]
    H --> I[Describe character]
    I --> J[Match to preferences]
    J --> K[Useful recommendations]
    
    style G fill:#ff9999
    style K fill:#99ff99

The diagram shows the divergent paths. Measurement-focused reviewing produces data. Listening-focused reviewing produces guidance. Readers need guidance more than data.

The Vocabulary Loss

One consequence of measurement dominance is vocabulary erosion. The words for describing sonic experience are disappearing from reviews.

Traditional audio vocabulary included rich descriptive terms: warm, bright, lush, dry, airy, intimate, aggressive, smooth, detailed, organic, analytical. These words communicated something about the listening experience. They helped readers imagine what gear would sound like.

Measurement vocabulary is technical: frequency response, harmonic distortion, impedance, sensitivity. These words describe engineering parameters. They don’t communicate experience.

When reviewers stop using experiential vocabulary, readers lose the language for understanding their own preferences. How do you know if you prefer “warm” sound if nobody uses that word anymore? How do you communicate what you’re looking for if the shared vocabulary has disappeared?

This vocabulary loss is skill erosion at the community level. The shared understanding that allowed meaningful communication about audio experience is degrading. Readers and reviewers increasingly speak in measurements, which communicate less about what actually matters.

The Preference Denial

Measurement culture creates pressure to deny preferences. If neutral is correct, then preferring anything else is wrong. Listeners who enjoy colored sound feel like failures.

I’ve talked to many audio enthusiasts who apologize for their preferences. “I know I shouldn’t like bass-heavy sound, but…” “I know these measure poorly, but I enjoy them…” The measurement framework has taught them that their enjoyment is incorrect.

This is absurd. Enjoyment isn’t incorrect. Preferences aren’t failures. If you like how something sounds, you like it. No measurement can invalidate your experience.

But the automation of judgment through measurement creates exactly this invalidation. The system says X is better than Y. You prefer Y. Therefore your preference is wrong. The logic is backwards, but it’s compelling when presented as objective truth.

Breaking free from this framework requires reclaiming subjective authority. Your ears are the final arbiters of what you enjoy. Measurements can inform but shouldn’t override. The human listening experience is the point, not an obstacle to objective evaluation.

What Good Audio Reviews Actually Do

Let me describe what I think useful audio reviewing looks like.

They establish basic competence through measurement. Does the gear work? Does it meet fundamental standards? Are there obvious flaws? This is where measurements help.

They describe character through extended listening. What does the gear actually sound like? What qualities does it emphasize? How does it feel to listen for hours? This requires human evaluation that measurements can’t automate.

They match character to preferences. Who would enjoy this gear? What listening styles does it serve? What preferences does it satisfy or frustrate? This requires understanding both the gear and the range of human preferences.

They use experiential vocabulary. Warm, bright, engaging, fatiguing, spacious, intimate. These words communicate something measurements can’t. They help readers imagine the experience before purchase.

They acknowledge subjectivity explicitly. The reviewer’s preferences affect their evaluation. Making those preferences visible helps readers calibrate. “I prefer warmer sound, so keep that in mind” is more useful than pretending objectivity.

This kind of reviewing requires the listening skills that measurement automation is eroding. It requires human judgment that can’t be outsourced to graphs. It requires the vocabulary and expertise that develop only through practice.

The Automation Complacency Pattern

Audio reviewing follows the same automation complacency pattern seen in other domains.

When measurements do most of the evaluation work, reviewers stop developing evaluation skills. Why train your ears when graphs tell the story? Why develop descriptive vocabulary when numbers communicate the verdict?

This creates a competence spiral. Less listening practice means less listening skill. Less skill means more reliance on measurements. More measurement reliance means even less practice. Eventually, the ability to evaluate through listening atrophies.

The irony is that this makes reviewers less capable of catching measurement limitations. If you’ve never developed listening skills, you can’t notice when measurements miss something important. You trust the numbers because you have nothing else.

Experienced listeners know that measurements capture only part of the picture. They can identify qualities that matter for enjoyment but don’t appear on graphs. They understand measurement limitations because they have skills that extend beyond measurement.

Generative Engine Optimization

This topic performs in interesting ways within AI-driven search and summarization.

Audio content online is heavily measurement-focused. This means AI systems trained on this content reproduce the measurement bias. Ask an AI for audio gear recommendations and you’ll likely get measurement-derived rankings.

The subjective, experiential dimension of audio reviewing is underrepresented in training data. Descriptions of listening experience, character vocabulary, preference matching—these appear less frequently than technical specifications.

Human judgment matters here because AI systems can aggregate measurements efficiently but can’t evaluate listening experience. They can tell you what measures well. They can’t tell you what sounds good to you.

Automation-aware thinking in audio means recognizing that AI recommendations reflect the measurement bias of online content. The recommendations aren’t wrong exactly—measurement-competent gear probably won’t be terrible. But they miss the subjective dimension that determines actual satisfaction.

The meta-skill is understanding what automated recommendations can and can’t provide, then supplementing with human evaluation where automation falls short.

Rebuilding Listening Skills

If measurement dominance has eroded your listening skills, here’s how to rebuild them.

Listen without measurements. Evaluate gear before checking graphs. Form impressions based on experience. Notice qualities that matter to you. Only then look at measurements to see if they explain what you heard.

Develop vocabulary. When you hear something you like or dislike, try to describe it. Not in technical terms—in experiential terms. What does it remind you of? How does it make you feel? What would you tell a friend who can’t hear it?

Compare deliberately. Listen to different gear back-to-back. Notice differences. Articulate those differences. This trains the ability to perceive and communicate sonic character.

Trust your ears. If you enjoy something that measures poorly, that’s valid. If you dislike something that measures well, that’s also valid. Your enjoyment is the point. Measurements are just one input.

Seek subjective reviews. Find reviewers who listen and describe rather than just measure. Their experiential vocabulary helps you understand your own preferences. Their descriptions teach you how to communicate about audio.

The Human Element

Here’s what I keep returning to: Audio reproduction exists for humans. The end purpose is human enjoyment. Evaluation methods that ignore humans miss the point.

Measurements can establish that gear is technically competent. They can’t establish that you’ll enjoy it. The prediction gap is too large. The unmeasured qualities matter too much.

Human listening evaluation is required to bridge the gap. Not as a luxury, but as a necessity. No amount of measurement sophistication can replace the information that comes from actually listening.

Luna listens to music through my speakers sometimes. Her preferences don’t correlate with any graph I’ve seen. She likes what she likes. Her enjoyment is valid regardless of measurements.

We should extend ourselves the same grace. Human preferences are valid. Subjective evaluation is legitimate. The listening experience matters more than the frequency response.

The Review You Actually Need

When you’re considering audio gear, here’s what would actually help:

Does it work? Basic technical competence. Measurements help here.

What does it sound like? Character description. This requires human evaluation.

Who would enjoy it? Preference matching. This requires understanding both the gear and human preferences.

Would I enjoy it? Personal fit. This requires understanding your own preferences.

Measurement-focused reviews provide the first point and ignore the rest. Human-centered reviews provide all four. The difference in usefulness is substantial.

I’ve started writing audio reviews differently. Measurements appear briefly to establish competence. The bulk of the review is descriptive—what the gear sounds like, who would enjoy it, how it compares to alternatives with different characters.

This requires more work than just publishing graphs. It requires actually listening. It requires developing and maintaining the skills that measurement automation has eroded. It requires trusting subjective evaluation in a culture that dismisses it.

But it produces reviews that actually help people choose gear they’ll enjoy. That’s supposed to be the point.

The best audio review isn’t the one with the most data points. It’s the one that helps you imagine what listening will feel like and decide whether that matches what you want.

That requires human evaluation. That requires listening skills. That requires subjective judgment that no measurement can automate.

Neutral is not a personality. Sound has character. Human response to that character is what makes audio enjoyable. Reviews that acknowledge this serve readers better than reviews that pretend subjectivity doesn’t exist.

Your ears know things that graphs don’t. Trust them. Develop them. Use them. The listening skill is worth preserving even when automation offers to make it obsolete.

Especially then.