Noise Frequency Analyzer
Identify mystery noises by their frequency fingerprint. Capture a quiet baseline, then record the problem noise to see exactly what changed. The tool matches against 30+ known noise signatures, classifies noise color (white/pink/brown), and provides actionable troubleshooting advice — all processed locally in your browser.
Noise Frequency Analyzer Tool
| Noise Source | Freq Range | Status |
|---|
| Time | Source | Freq | Confidence |
|---|
How to Use the Noise Frequency Analyzer
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Start Listening
Click "Start Listening" and grant microphone permission. Select your preferred microphone from the dropdown if needed. The live spectrum begins immediately.
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Capture Baseline
In a quiet moment (when the problem noise is absent), click "Capture Baseline". The tool records 3 seconds of ambient sound as your reference point.
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Capture the Noise
When the problem noise is present, click "Capture Noise". The tool records 3 seconds and computes the difference spectrum, highlighting exactly which frequencies the noise adds.
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Review the Diagnosis
The tool matches detected frequencies against 30+ noise signatures and shows the most likely source with its icon, description, and a specific troubleshooting fix. The noise color classification tells you the spectral character.
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Export or Copy Results
Use "Copy Noise Report" for a quick summary or "Export CSV" to download the full detection history for documentation or further analysis.
Understanding Your Results
Noise Signature Matching
The tool maintains a database of 30+ common noise sources, each defined by a characteristic frequency range. When your audio contains energy in one of these ranges above the threshold, the tool identifies the likely source and provides a confidence percentage based on how closely your signal matches the known pattern.
Baseline vs Noise Comparison
The difference spectrum subtracts your baseline recording from the noise recording, showing only the frequencies that changed. This eliminates ambient sounds (air conditioning, computer fans, etc.) and isolates the actual problem noise, making identification far more accurate.
Noise Color Classification
Noise is classified by its spectral slope — how energy is distributed across frequencies:
- White noise has equal energy at all frequencies (flat slope, 0 dB/octave)
- Pink noise drops 3 dB per octave — equal energy per octave, common in nature
- Brown noise drops 6 dB per octave — deep, rumbling character like thunder
- Blue noise increases 3 dB per octave — hissy, high-frequency emphasis
Frequency Fingerprint
The fingerprint visualization shows the unique spectral shape of the detected noise as a compact barcode-style display. Each noise source has a characteristic fingerprint pattern that helps with visual identification even when multiple sources are present.
Confidence Level
The match confidence indicates how well the detected frequency pattern matches a known noise signature. Higher confidence means a stronger, cleaner match. Multiple simultaneous sources may reduce individual confidence scores.
Technical Background
Noise identification combines spectral analysis with pattern matching. The tool analyzes incoming audio in real time using the Web Audio API's FFT implementation and compares the spectral characteristics against known noise profiles.
Baseline Subtraction
The baseline capture stores the average power spectrum of your environment over 3 seconds. When you capture the noise, the tool computes the spectral difference in dB, revealing only the frequencies that increased above the ambient floor. This is the same principle used in professional noise analysis — similar to how noise-canceling headphones identify and remove unwanted sound.
Signature Matching Algorithm
Each noise signature in the database defines a frequency range and optional harmonic structure. The matcher calculates energy concentration within each range and checks for expected harmonic relationships (e.g., 60 Hz + 120 Hz + 180 Hz for mains hum). Confidence is weighted by how much energy falls within the expected range versus outside it.
Spectral Slope Estimation
Noise color is determined by fitting a linear regression to the log-frequency vs. dB power spectrum. The slope of this line classifies the noise: 0 dB/octave = white, -3 dB/octave = pink, -6 dB/octave = brown. The regression uses octave-spaced sample points for an unbiased estimate across the audible range.
Frequently Asked Questions
What is the difference between baseline and noise capture?
The baseline records the ambient sound when the problem noise is absent — your normal background. The noise capture records the sound when the problem is present. The tool then subtracts the baseline from the noise to isolate exactly which frequencies the problem noise adds, eliminating false matches from normal ambient sounds.
Do I need to capture a baseline to use the tool?
No — the tool works in real-time mode without a baseline. However, baseline capture significantly improves accuracy by removing ambient noise from the analysis. Without a baseline, the tool matches against the raw spectrum, which may include false matches from normal background sounds.
What are noise colors (white, pink, brown)?
Noise colors describe the spectral distribution of random noise. White noise has equal energy at every frequency (like TV static). Pink noise has equal energy per octave, sounding more balanced and natural (like rainfall). Brown noise has more energy at low frequencies, creating a deep rumble (like thunder or ocean waves).
Why does the tool detect 60 Hz or 50 Hz hum?
A persistent 60 Hz (North America) or 50 Hz (Europe/Asia) tone is electrical mains hum caused by electromagnetic interference from power lines, transformers, or cables. The harmonics at 120/100 Hz are also common. Use shielded cables, a ground loop isolator, or move away from power sources.
How many noise signatures does the database contain?
The database contains 30+ noise signatures covering electrical hum (50/60 Hz and harmonics), ground loops, fluorescent lighting, HVAC systems, refrigerators, computer fans, dimmer switches, transformers, CRT whine, USB power supply noise, water pipe vibration, appliance motors, and more. Each signature includes the frequency range, description, and a specific troubleshooting fix.
Can this tool identify multiple noise sources at once?
Yes. The tool scans for all matching signatures simultaneously and lists every match with its confidence level. For example, it can identify both a 60 Hz mains hum and a 120 Hz ground loop buzz in the same audio signal, showing each match in the results.
Is my audio data private?
Absolutely. All frequency analysis and noise matching runs 100% in your browser using the Web Audio API. No audio data is ever recorded, transmitted, or stored on any server. The tool works completely offline once loaded.
What microphone should I use for best results?
Any microphone works, but results improve with a flat-response measurement microphone or a good-quality condenser microphone. Built-in laptop microphones tend to have poor low-frequency response, which may miss sub-bass noise sources. An external USB microphone placed close to the noise source gives the best results.
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