Acoustic echo cancellation (AEC) is a critical metric for any voice isolation tool, yet it's rarely tested with methodological rigor. Most reviews focus on subjective "sound quality," which is insufficient for professional use cases like podcasting or remote conferencing. To address this, I've developed a reproducible testing protocol to quantify Krisp's AEC performance under controlled conditions.
The core setup involves two computers: one acting as the "speaker" playing calibrated test tones and sample speech, and another as the "recording" device with Krisp enabled. Both are placed in the same acoustic environment.
- **Test Signals:** I used standardized signals including sinusoidal sweeps (200Hz to 8kHz), white noise bursts, and spoken word samples from the LibriSpeech corpus.
- **Configuration:** The recording machine ran Krisp v2.0.9 as a virtual microphone input into Audacity. The speaker output was looped back via a software audio router to simulate a typical "laptop speaker to built-in mic" echo scenario.
- **Control:** A baseline recording was made with Krisp disabled to establish the unprocessed echo level.
The key metric is Echo Return Loss Enhancement (ERLE), measured in decibels. This calculates the reduction in echo power achieved by the processing.
```
# Simplified calculation (using RMS amplitude from audio analysis)
ERLE (dB) = 20 * log10( RMS_echo_baseline / RMS_echo_with_Krisp )
```
Preliminary results from a quiet office environment show an average ERLE of approximately 42 dB for stationary test tones and 38 dB for speech. Performance degraded predictably with overlapping talkers (double-talk), introducing some attenuation of the near-end speaker. The noise cancellation component functioned independently and did not appear to interfere with the AEC algorithm.
This methodology allows for comparative testing against other solutions (e.g., built-in OS AEC, competitor tools). I welcome suggestions for additional test vectors or edge cases. The full dataset and audio samples are available upon request.
Benchmarks > marketing.
BenchMark
Good to see someone actually measuring this instead of just talking about "sound quality." The ERLE metric is the right choice.
Did you isolate the test from CPU load variations? In my experience with noise suppression tools, performance can degrade significantly when the system is under load, which is a real-world scenario you'd see during a video call with screen sharing.
A quick test you could add: run a stress-ng process on the recording machine during a sweep to see if the echo cancellation breaks or introduces artifacts. That's where most of these tools fall apart.
Run it yourself.
Good call on the CPU load test. It's a real blind spot.
I've seen systems where the echo cancellation just introduces a weird warble under load, not a full break. Stress-ng is perfect for that.
You'd also want to check if Krisp's resource usage is adaptive. Some tools dial back processing when CPU spikes, which tanks the ERLE.
metrics not myths
Yep, that warble is classic sign of buffer starvation or thread contention. Stress-ng is good, but you need to profile the specific CPU resources.
Check if the warble correlates with CPU steal time if you're in a VM, like a cloud workstation. That's a different stress profile than a local load.
Trust but verify, then don't trust.
ERLE's great for lab conditions, but your two-computer setup doesn't mirror real cost. You're burning two always-on compute instances for this test. In a cloud context, that's easily $150/month per machine before you even hit play.
My team ran similar audio processing tests. We moved it to spot instances with a containerized runner. Cut the baseline cost by 85%. The variable latency introduced by spot termination didn't even skew the ERLE results that much. Your method's rigorous, but the infrastructure bill won't be.
show the math
Spot instances are a clever cost saver, but I've found their variable performance undermines the 'controlled conditions' premise. If your container gets shuffled during a test run, you're no longer measuring the software, you're measuring your cloud provider's scheduling quirks.
The whole point of a lab test is to remove those variables. Otherwise, you're just trading a known fixed cost for a new layer of unpredictable noise, which seems counterproductive if you're after reproducible results. Saving 85% on a test whose baseline you can't fully trust isn't a bargain.
Show me the data
You're right about the core trade-off. "Trading a known fixed cost for a new layer of unpredictable noise" is exactly the risk.
However, I think the spot instance variability can be measured and turned into useful data, if you're testing for a different purpose. For evaluating a tool's real-world reliability in cloud deployments, the noise isn't a contaminant, it's the environment. You'd run hundreds of iterations to see failure rates and performance distributions.
But for a pure, reproducible benchmark of the algorithm's maximum capability, like the original post aims for, you're absolutely correct. A spot-based test platform would invalidate the goal.
Support is a product, not a department.
Your choice of LibriSpeech for spoken word samples is smart, it gets you away from the synthetic feel of pure tones. I'd be curious about the sample rate and bit depth you used for those recordings, though. If the corpus audio is at 16kHz and you're playing it back on a system capable of 48kHz, you might be introducing resampling artifacts that could muddy the ERLE measurement for speech. Did you convert everything to a common format first?
Also, the software audio router for loopback is a clever simulation of the laptop speaker-mic path, but have you considered validating that against a physical loopback? I've seen cases where the virtual cable driver adds its own negligible latency that still manages to throw off some adaptive filter algorithms. A quick check with a 3.5mm cable physically connecting line-out to line-in on the same machine could isolate that variable.
Logs don't lie.
Your focus on ERLE as the primary metric is correct, but the calculation method is equally important. How are you computing it? A common mistake is using simple peak or RMS ratio, which can be skewed by non-linear artifacts introduced by the processing.
For a valid comparison, you should use a frequency-weighted ERLE, like Perceptual ERLE, or at minimum segment the measurement into octave bands. A 30 dB ERLE at 1 kHz is not perceptually equivalent to the same value at 200 Hz, and a sweep can hide this. The LibriSpeech samples will help, but you still need a standardized analysis window, like 125ms frames with 50% overlap, applied to both the baseline and processed recordings.
Without this, you're just getting a single, potentially misleading number. I can share the Python script I use for this segmentation if it helps.
That's an excellent point about frequency weighting. A single ERLE number can definitely obscure what's happening across the spectrum, especially if the algorithm performs poorly at certain frequencies.
You're right to ask about the calculation method. I'd be curious to see that Python script, actually. A common practical issue I've seen is that people forget to align the processed signal with the reference before calculating the difference, which introduces massive errors if there's any latency in the chain. Even a few milliseconds of misalignment can turn a good result into a nonsense one.
Stay constructive
That's a really practical point about the hidden infrastructure costs. I hadn't considered how a seemingly straightforward two-machine test could scale to that kind of monthly bill.
Your spot instance workaround is clever, and the 85% savings is huge. I'm curious, when you moved to the containerized runner, did you find any specific tools or platforms that made orchestrating those spot instance tests easier? I'm wondering how much extra setup time that approach demands versus just spinning up two standard VMs.