If you’ve built (or evaluated) a speech enhancement model, you’ve probably seen this pattern:

• The enhanced spectrogram magnitude looks cleaner.
• Objective noise metrics improve.
• But the audio still sounds “watery,” “phasey,” or oddly smeared—especially at very low SNR.

That’s not a coincidence. In low SNR conditions, phase becomes the deciding factor between “looks good” and “sounds good.”

This post breaks down why phase matters, what typically goes wrong when we ignore it, and how a simple experiment makes the point uncomfortably clear:

Bad phase ruins good magnitude.

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Why phase is a big deal (in plain engineering terms)

Most modern enhancement systems work in a time–frequency representation (like an STFT or similar). In that world, each small time slice is described by:

• Magnitude: how much energy is present in each frequency region
• Phase: how those frequency components align in time so they add up into a waveform

Magnitude tells you what’s present.

Phase tells you how it comes together.

In moderate noise, using the noisy phase is often “good enough.” In very noisy conditions, it stops being good enough.

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The low SNR trap: why “noisy phase is fine” fails

Low SNR (think: background noise as loud as speech, or louder) changes the game in a few important ways.

1) Noise dominates more of the time–frequency plane

At high SNR, many regions are speech-dominant: phase is somewhat aligned with speech structure.

At low SNR, a large fraction of regions are noise-dominant. In those regions:

• the phase is driven mostly by noise
• the speech contribution is weak or intermittent
• the “timing” information becomes unreliable

So even if your model does a great job estimating magnitude, reusing noisy phase means you’re reconstructing speech with noise-controlled alignment.

2) Listening artifacts become obvious when enhancement is aggressive

Low SNR enhancement usually requires strong attenuation, mask sharpening, or heavy suppression.

That’s exactly when phase errors become most audible. Common symptoms:

• “watery / underwater” sound
• “hollow” or “metallic” timbre
• “swirliness”
• smeared attacks (plosives) and softened consonants

People often assume these are just “mask artifacts.” Many of them are really phase–magnitude mismatch artifacts.

3) Consonants pay the price

Unvoiced consonants like “s”, “sh”, “f”, and bursts like “t”, “k”, “p” carry key intelligibility cues.

At low SNR they are already difficult:

• they’re noise-like
• they occupy broader bands
• they’re short and transient

If phase is inaccurate, these cues get blurred or shifted in time, and intelligibility drops even when the speech is louder or the background seems reduced.

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A simple experiment that isolates phase (your key observation)

Here’s the most convincing way I’ve found to explain phase importance—because it removes “maybe it was the model” ambiguity.

The experiment idea

You take the same estimated magnitude (from your enhancement system) and do two reconstructions:

1) Estimated magnitude + noisy phase

2) Estimated magnitude + clean phase

You don’t change the magnitude estimate at all. You only change the phase used for reconstruction.

What we observed

Experiments show that estimated magnitude combined with noisy phase yields lower intelligibility than the same estimated magnitude combined with clean phase—particularly in very noisy conditions.

That’s the punchline. Because it proves:

• Your magnitude estimate can be “good”
• Yet the final output can still be poor
• And the difference is driven mainly by phase

So yes:

Bad phase ruins good magnitude.

Why the gap widens at very low SNR

At very low SNR, the noisy phase becomes more random or more noise-dominant across more regions. So the reconstruction becomes increasingly misaligned with speech structure.

In other words:

• the cleaner the magnitude becomes (relative to noise), the more obvious it is that the timing is wrong
• phase errors become the limiting factor

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Why this matters for real products (not just papers)

In dev-focused terms: this isn’t a theoretical nit.

If you’re building enhancement for:

• headsets / earbuds
• conferencing devices
• voice recorders
• in-car voice
• smart assistants in noisy rooms

…users don’t care that your magnitude loss improved. They care that:

• speech is understandable
• consonants are crisp
• the sound isn’t fatiguing
• the output doesn’t feel “synthetic”

Phase is central to those outcomes at low SNR.

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Common failure modes when phase is ignored

Here are some recognizable “symptoms” that often indicate phase is the bottleneck:

• Spectrogram looks clean but audio sounds smeared
• Unvoiced consonants disappear or turn harsh
• Speech sounds thin/hollow
• Warbly musical artifacts appear
• The output is “cleaner” but harder to follow
• Users complain about listening fatigue even when noise is reduced

If any of these match your system, it’s worth examining phase handling.

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What modern phase-aware enhancement looks like (practical view)

You don’t need to become a phase purist overnight. There are several ways teams typically move beyond the “noisy phase” baseline.

1) Predict more than magnitude

Instead of only estimating “how much to keep,” many models estimate representations that include timing/alignment information.

This often improves:

• transient clarity
• consonant intelligibility
• reduction of “phasey” artifacts

2) Use phase-aware training objectives

Even if your model outputs something mask-like, training it with objectives that correlate with waveform fidelity helps reduce the mismatch that causes artifacts.

3) Add a refinement stage

A lightweight second stage can:

• fix reconstruction inconsistencies
• suppress residual artifacts
• stabilize output quality at the worst SNRs

4) Time-domain enhancement

Waveform models handle phase implicitly because they directly output audio samples.

They can be strong at low SNR, but you’ll want to balance:

• compute
• latency
• stability across diverse noise types

5) Multi-mic systems: phase is also spatial

If you’re using multiple microphones, phase differences contain spatial cues. Mishandling phase can:

• degrade beamforming
• break spatial realism
• cause unstable localization

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How to evaluate phase impact in your own system

If you want a quick, convincing internal demo (great for alignment with stakeholders), try:

• Pick several low SNR clips (babble, street, cafeteria)

• Run your enhancement model to get an estimated magnitude

• Reconstruct two versions:

• with noisy phase

• with clean phase (for analysis only, because you don’t have clean phase at runtime)

Then do:

• A/B listening
• intelligibility scoring (even informal word accuracy is useful)
• consonant-focused listening checks (“s”, “sh”, “t”, “k” clarity)

If the clean-phase reconstruction is substantially better, you’ve proven the phase bottleneck—and you have a clear direction for improvement.

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Key takeaway

At low SNR, enhancement quality is not determined by magnitude alone. Your experiment highlights it perfectly:

Even with the same estimated magnitude, using noisy phase reduces intelligibility compared to using clean phase—especially in very noisy conditions.

So the next time your model “looks great” but sounds disappointing, don’t just tune the mask.

Look at phase.