Silence the Wind, Amplify Your Ride™

Calculating wind noise reduction can be confusing because it involves both a logarithmic scale and a frequency-weighted measurement that mimics human hearing.  This page details the methodologies used to quantify noise reduction, focusing on how decibel measurements, particularly dB(A), are converted into meaningful percentage values.  We will cover the calculations that underpin these metrics, providing clarity on the difference between a percentage reduction in sound pressure and a percentage reduction as perceived by the human ear.

 

Cycling ear-wind noise is primarily comprised of pseudo sound and dipole acoustic sound:

 

• Pseudo-Sound:  This refers to the direct, non-propagating pressure variations within the turbulent airflow immediately surrounding and impinging upon the ear canal opening. Think of it as the chaotic "whooshing" sensation you feel and hear directly on your ear. As we've noted, pseudo sound is hydrodynamic.

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• Dipole Acoustic Sound:  As airflow separates and sheds vortices from various parts of the cyclist's head and helmet (e.g., ear, helmet edges, straps), these unsteady fluid-structure interactions can generate propagating acoustic waves. These are sound waves that travel to your eardrum. As we've noted, dipole is acoustic.

A microphone, placed correctly in the ear canal, can accurately capture both of these sources.

Microphones

• Professional ultra-high-sensitivity microphones

• In the canal placement for accurate measurements*

• Pre-test microphone calibration is critical for accuracy

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*In the canal microphone placement ensures the most accurate and representative measurement of the sound pressure level reaching the eardrum, directly accounting for the unique acoustic properties and resonances of an individual's ear canal and offering a degree of natural protection from the direct turbulent wind flow.  Incorrect placement outside the ear canal can produce overstated wind noise reduction performance measurements.

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Testing Environments:

• Wind Tunnel:  Our custom aeroacoustic (open-wall) wind tunnel ensures consistent and repeatable testing. 

• Real-World Testing:  While less controlled, real-world testing on a bicycle provides valuable data under actual riding conditions.  This involves riding at consistent speeds on a predefined course and recording wind noise.

Data Acquisition: The microphones capture the sound as an analog electrical signal, which is then converted into a digital signal using an Analog-to-Digital Converter.  This digital data, representing the sound pressure fluctuations over time, is then ready for analysis.  We use a Zoom audio recorder and TrueRTA, Sigview, and MATLAB for signal analysis.

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Pascals (Pa) => Decibels (dB)

The fundamental unit of pressure is the Pascal (Pa), which is defined as one newton of force per square meter (1 N/m^2). For sound, we're talking about incredibly small pressure fluctuations.  The reference point for 0 dB SPL, the threshold of human hearing, is 20 micropascals (20 \mu Pa).  A micropascal is one-millionth of a Pascal.  This is an extremely small pressure fluctuation.  On the other end of the scale, the threshold of pain for human hearing is roughly 20 Pascals (Pa), which corresponds to about 120 dB SPL.

Putting this into perspective, the difference in pressure between a whisper and a jet engine is not linear.  A 20 Pa sound pressure is one million times greater than a 20 \mu Pa sound pressure.  This is precisely why the decibel scale is so useful.  Instead of dealing with the unwieldy range of 20 \mu Pa to 20 Pa and all the zeros in between, we can use a simple, logarithmic scale from 0 dB to 120 dB.  The microphone, acting as a pressure sensor, detects these Pascal fluctuations, and the decibel scale is the convenient measurement system we use to interpret and quantify them.

 

To measure and analyze cycling wind noise, acoustic measurement is combined with signal processing techniques. Decibels dB(A) are used to quantify the loudness, while Fast Fourier Transform (FFT) helps deconstruct its frequency components.  This detailed FFT analysis is critical for evaluating the performance of wind noise reduction solutions.