January 13, 2026

ISO 532 & ECMA-74 Sound Quality Measurement with OpenTest

This article is for engineers working in acoustics and vibration testing. It introduces how to perform sound quality measurements in OpenTest based on the ISO 532 loudness standard and the ECMA-74 tonality evaluation methods. By measuring and comparing three key psychoacoustic metrics — Loudness, Sharpness, and Prominence (Tonality) — teams in consumer electronics, automotive NVH, home appliances and IT equipment can turn “how good or bad it sounds” into quantitative engineering data, and complete a standardized sound quality workflow on a single platform from data acquisition, through analysis, to reporting.

Why Sound Quality Measurements Matter

In traditional noise testing, we usually rely on dB values to describe how “loud” a device is. But more and more studies and real-world projects are reminding engineers that “loudness” is only part of the story. In automotive NVH, home appliances, IT equipment and consumer electronics, user acceptance of product sound depends much more on whether it sounds pleasant, sharp, tiring or annoying, not just the overall sound pressure level.

Industry surveys also show that most manufacturers now treat “how good it sounds” as being just as important as “how quiet it is”, and they start paying attention to sound quality already in early design phases. At the same sound level, poor sound quality can significantly drag down overall product satisfaction.

This is exactly why Sound Quality as a discipline exists: through a set of psychoacoustic metrics such as Loudness, Sharpness and Tonality/Prominence, it turns subjective impressions like “sharp”, “boomy”, “harsh” or “smooth” into data that is measurable, comparable and traceable, so engineering teams can go beyond noise control and truly design and optimize product sound around listening experience.

Key Metrics in Sound Quality Measurement

In engineering practice, sound quality is not a single number, but a set of psychoacoustic quantities. Commonly used metrics include Loudness, Sharpness, Roughness, Fluctuation Strength, Prominence/Tonality, etc.

Figure 1 – Key metrics in sound quality measurement

Loudness (ISO 532-1)

Loudness and Loudness Level describe how loud a sound is perceived by the human ear, rather than just its sound pressure level in dB. Internationally, the ISO 532-1:2017 standard based on the Zwicker method is widely used for loudness calculation. It can handle both stationary and time-varying sounds and correlates well with subjective perception in many technical noise applications.

From an engineering point of view, loudness has clear advantages over A-weighted SPL:

It accounts for the ear’s different sensitivity to frequency (human hearing is more sensitive in the mid-high range)
At the same dB level, loudness often tracks “does it feel loud or not?” more accurately

Sharpness (DIN 45692)

Sharpness reflects whether a sound is perceived as sharp or piercing. When the high-frequency content has a higher proportion, people tend to feel the sound is more “sharp” or “edgy”.

Sharpness was standardized in DIN 45692:2009, and is typically calculated based on the specific loudness distribution from a loudness model, applying additional weighting in the higher Bark bands. The result is expressed in acum.

In applications such as fans, compressors and e-drive whine, reducing sharpness often improves subjective comfort more effectively than just lowering the overall dB level.

Roughness (asper)

Roughness corresponds roughly to fast amplitude modulation in the 15–300 Hz range, which gives a “raspy, vibrating” impression — for example in certain inverter whines or gear whine where the sound feels like it is “shaking”.

Unit: asper
Classical definition: 1 asper corresponds to a 1 kHz, 60 dB pure tone amplitude-modulated at about 70 Hz with 100% modulation depth
The deeper the modulation and the closer the modulation frequency is to the sensitive region (around 70 Hz), the higher the perceived roughness

In engineering, roughness is often used to describe how much a sound feels like it is “buzzing” or “scratching”, and it is particularly relevant for subjective evaluation of technical noise in e-drive systems, gearboxes and compressors.

Fluctuation Strength (vacil)

Fluctuation Strength captures slower amplitude fluctuations — amplitudes that go up and down in the range of roughly 0.5–20 Hz, perceived as “pulsing” or “breathing”, with a typical peak sensitivity around 4 Hz.

Unit: vacil
A classical definition of 1 vacil: a 1 kHz, 60 dB pure tone with 4 Hz, 100% amplitude modulation
In cabin idle “breathing noise”, or fans whose level periodically rises and falls, fluctuation strength is a key descriptor

You can think of Fluctuation Strength and Roughness as two sides of the same “modulation” coin:

Fluctuation Strength: slow modulation (a few Hz), perceived as “breathing” or “pulsing”
Roughness: faster modulation (tens of Hz), perceived as “vibrating, raspy, grainy”

Prominence / Tonality (ECMA-74)

Many devices are not particularly loud overall, yet become extremely annoying because of one or two narrowband tonal components. These “sticking out tones” are usually quantified by Tonality / Prominence.

In IT and information technology equipment noise, ECMA-74 specifies methods based on Tone-to-Noise Ratio (TNR) and Prominence Ratio (PR) to evaluate tonal prominence and to determine whether a spectral line is a “prominent tone”.

Historically, these metrics come from psychoacoustic research and are now widely used in automotive, aerospace, home appliances and IT equipment to predict and optimize annoyance. For example, studies have shown that, with loudness controlled, Sharpness, Tonality and Fluctuation Strength are important predictors for the annoyance of helicopter noise.

Why Sound Quality Is More Useful Than Just “Watching dB”

In many projects, you may have already seen questions like these:

Two fan designs have similar sound power levels, but one “sounds smooth” while the other has a clear whine
After noise reduction, overall SPL is a few dB lower, but user feedback hardly improves
On the production line, A-weighted SPL is used as the only criterion, and some “bad-sounding” units still slip through

Fundamentally, that is because:

Sound pressure level / sound power = “how much energy is there”
Sound quality metrics = “how the ear feels about it”

With metrics like Loudness, Sharpness, Roughness, Fluctuation Strength and Prominence, you can decompose vague complaints like “it just sounds uncomfortable” into:

Which frequency region has too much energy (leading to high sharpness)
Whether there is strong amplitude modulation (causing high roughness or fluctuation strength)
Whether any tonal component is sticking out clearly above its surroundings (high tonality / prominence)

In engineering iteration, these metrics can be mapped directly to:

Structural optimization (stiffness, modes, blade shape, etc.)
Control strategies (e.g. PWM frequency, fan speed curves and transitions)
Material and noise treatment / isolation choices

This gives you much clearer and more actionable directions than “just reduce dB”.

Sound Quality Analysis in OpenTest

As a platform for acoustics and vibration testing, OpenTest supports a complete sound quality workflow from acquisition → analysis → reporting. Fill in the form at the bottom ↓ of this page to contact us and get an OpenTest demo.

Example Device: Office PC Fan Noise

To make the process concrete, we use a very accessible device as our example: a typical office PC.

Test objective: evaluate sound quality metrics of its fan noise under different operating conditions, in order to:

Compare subjective noise performance of different cooling and fan control strategies
Provide quantitative input to NVH reviews (e.g. does loudness exceed the target, is sharpness too high?)
Build a foundation for further sound quality optimization (e.g. suppressing whine frequencies, smoothing speed transitions)

Test environments might be:

A semi-anechoic room / low-noise lab (recommended); or
A quiet office environment for early-stage, comparative evaluation

Measurement System: SonoDAQ + OpenTest Sound Quality Module

On the hardware side, we use a CRYSOUND SonoDAQ multi-channel data acquisition system (for more detailed model information, please contact us), together with one or more measurement microphones placed near the PC fan or at the listening position, according to the test requirements.

Figure 2 – SonoDAQ Pro multi-channel data acquisition system

Of course, OpenTest also supports connection via openDAQ, ASIO, WASAPI and other mainstream audio interfaces, so you can reuse existing DAQ devices or audio interfaces for measurement where appropriate.

On the software side, the Sound Quality module in OpenTest is one of the measurement modules. Combined with FFT analysis, octave analysis and sound level analysis, it can cover most standard audio and vibration test needs.

Configuring Measurement Parameters

After creating a new project in OpenTest, proceed as follows:

1. Channel configuration and calibration

In Channel Setup, select the microphone channels to be used and set sensitivity, sampling rate and frequency weighting as required
Use a sound calibrator (e.g. 1 kHz, 94 dB SPL) to calibrate the measurement microphones, ensuring that loudness and related metrics have a reliable absolute reference

2. Switch to the “Measure > Sound Quality” module

Select the metrics to be calculated: Loudness, Sharpness, Prominence
Set analysis bandwidth, frequency resolution and time averaging modes
Optionally configure test duration and labels for different operating conditions

Essentially, this step turns the “calculation definitions” in ISO 532, DIN 45692 and ECMA-74 into a reusable OpenTest sound quality scenario template.

Acquiring Sound Data for Different Operating Conditions

Once the test environment is set up and the parameters are configured, click Start to measure sound quality data under different operating conditions. Each test record is saved automatically for later analysis.

Because sound quality focuses on how it sounds during real use, it is recommended to record several typical conditions, for example:

Idle / standby (fan off or low speed)
Typical office load (documents, multi-tab browsing, etc.)
High load / stress test (CPU/GPU at full load)

With this breakdown, engineers can clearly manage which sound quality result corresponds to which operating condition.

Figure 3 – Overlaying multiple sound quality test records in OpenTest

From Multiple Measurements to One Sound Quality Report

After measuring multiple operating conditions (e.g. idle, typical office and full-load stress test), you can do the following in OpenTest.

In the data set list, select the records you want to compare and overlay:
- Compare loudness curves under different conditions
- See whether sharpness spikes during acceleration or speed transitions
- Identify conditions where prominent narrowband tones appear (high prominence)
In the Data Selector, save the associated waveforms and analysis results:
- Export .wav files for later listening tests or subjective evaluations
- Export .csv / Excel for further statistics or modelling
Click the Report button in the toolbar:
- Enter project, DUT and operating condition information
- Select sound quality metrics and plots to include (e.g. loudness vs. time, bar charts of sharpness, spectra with marked tonal prominence)
- Generate a sound quality report with one click for internal review or customer submission

Figure 4 – Example of a sound quality report in OpenTest

The generated report includes measurement conditions and operating modes, key sound quality metrics such as Loudness, Sharpness and Prominence, as well as a comparison with traditional acoustic metrics (sound pressure level, 1/3-octave spectra, sound power, etc.), making it easier for project teams to discuss using a set of metrics that are both objective and closely related to perceived sound.

Typical Application Scenarios

You can build different sound quality test scenarios in OpenTest for different businesses, for example:

Consumer electronics / IT equipment (laptops, routers, fans, etc.)
- Use loudness + sharpness + (where applicable) roughness to evaluate the “subjective comfort” of different thermal / fan strategies
- Compare sound quality across different speed curves or PWM schemes
Automotive NVH / e-drive systems
- Use multi-channel acquisition to record interior noise and speed signals synchronously
- Combine order analysis with sound quality metrics to see how “sharp” an e-drive whine is and whether there is pronounced modulation causing roughness
Home appliances and industrial equipment
- When sound power already meets standards, use sound quality metrics to further screen for “annoying noise”, instead of relying only on dB

If you are building or upgrading your sound quality testing capabilities, you can use ISO 532 and ECMA-74 as the backbone and let OpenTest connect environment, acquisition, analysis and reporting into a repeatable chain. That way, each sound quality test is clearly traceable and much more likely to evolve from a single experiment into a long-term engineering asset.

Welcome to fill in the form below ↓ to contact us and book a demo and trial of the OpenTest Sound Quality module. You can also visit the OpenTest website at www.opentest.com to learn more about its features and application cases.

Get in touch

Are you seeking more information about CRYSOUND’s solutions or need a demo? Contact us via the form bleow and one of our sales or support engineers will connect with you.

Connect Multiple DAQs and Audio Interfaces in OpenTest

In industrial testing, research, and quality validation, data acquisition devices (DAQs / audio interfaces / measurement microphone front-ends) are the “front door” of the entire system. As technology and applications become more specialized, a wide variety of DAQ devices has emerged: High-precision front-ends designed specifically for acoustics and vibration General-purpose dynamic signal acquisition modules Common USB sound cards and measurement microphones Hardware is not the bottleneck anymore. The real challenge is: How do you connect, configure, and manage devices from different brands and protocols in one software platform? OpenTest is built around this pain point. With an open, multi-protocol hardware access architecture, it turns acquisition from “isolated devices” into a unified platform, enabling cross-brand, multi-device data acquisition and analysis. Multi-Protocol Hardware Access: Reducing Vendor Lock-In OpenTest supports several mainstream connection methods. You can choose the appropriate protocol based on your hardware type and driver environment (actual compatibility depends on software version and device drivers): openDAQ – For open DAQ integration. Used to connect open hardware such as CRYSOUND SonoDAQ and manage channels and acquisition parameters in a unified way ASIO / WASAPI / MME / Core Audio – Mainstream audio interfaces on Windows and macOS, supporting professional audio interfaces and USB measurement microphones such as RME, Echo, miniDSP, etc. Other proprietary protocols – Can be added according to project requirements This means you no longer need to be locked into a single hardware brand or a single piece of software. Existing devices can be brought smoothly under one platform for centralized management. Multi-Device Collaboration: One Project, Many Acquisition Tasks Complex tests often require multiple signal sources to be acquired together, for example: Dynamic signals such as microphones and accelerometers Operating parameters such as speed, temperature, pressure, torque Auxiliary audio paths for monitoring and playback With OpenTest’s multi-protocol architecture, you can manage multiple devices within the same project. For NVH and structural testing, this kind of cross-device collaboration significantly reduces repetitive work like: Recording in multiple software tools → exporting → manual time alignment → re-analysis Getting Started: Connecting Devices Quickly Connect your data acquisition device to the PC running OpenTest USB connection, or Network connection (ensure the device and PC are on the same subnet) In the Hardware Setup panel, click the “+” icon in the upper-right corner. OpenTest will automatically scan for connected devices Check the devices you want to use and click Confirm to add them to the active device list Switch to the Channel Setup list, click the “+” icon in the upper-right corner, select the channels required for the current project (channels from different devices can be combined), and click Confirm to add them to the project Select the channels; OpenTest will automatically start real-time monitoring and analysis. You can then switch to different measurement modules according to your test needs Presets + Fine Tuning: Easy to Start, Easy to Standardize To help teams enter the testing state quickly, OpenTest supports a “presets + adjustments” configuration approach: Turn commonly used hardware parameters and acquisition settings into reusable templates Apply templates directly when creating a new project to avoid starting from scratch Still keep full flexibility to fine-tune settings for different operating conditions and devices For production line or regression testing, templating adds an important benefit: uniform test conditions, comparable results, and traceable processes across time and across operators. Logging and Monitoring: Designed for Long-Term Stability For long-duration, multi-device acquisition, the worst case is discovering that something dropped out halfway. OpenTest provides observability features to address this: Device and channel status monitoring – Quickly detect disconnections, overloads, and abnormal inputs Operation and error logs – Record key actions and error events to support troubleshooting and process optimization This is especially critical for continuous production testing and durability tests, significantly reducing the chance of “realizing halfway through that nothing was actually recorded.” Typical Application Scenarios Acoustics and vibration R&D – Use the same platform to connect front-end DAQs and audio interfaces, quickly complete acquisition, analysis, and report generation Automotive NVH / structural testing – Acquire noise, vibration, and operating parameters together, minimizing cross-software alignment work Production line automated testing – Template-based configuration + monitoring/logging + automated reporting to improve consistency and traceability OpenTest’s goal is not to make you replace all your hardware, but to bring your existing hardware together on one platform so that data acquisition becomes more efficient, more controllable, and much easier to standardize. Visit www.opentest.com to learn more about OpenTest features and hardware options, or contact the CRYSOUND team for demos and application support.

FFT Analysis with OpenTest

In audio and vibration testing, FFT analysis (Fast Fourier Transform) is one of the tools almost every engineer uses sooner or later: Loudspeaker frequency response Headphone distortion NVH diagnostics Structural resonance troubleshooting Production noise and “mysterious tone” hunting A lot of practical questions are actually asking the same few things: Where is the energy concentrated in frequency? Is it dominated by one tone or a bunch of harmonics? How high is the noise floor? Are there any resonance peaks? FFT is the most universal entry point to answer these questions. This article will help you clarify three things from an engineering perspective: What FFT analysis is How FFT works conceptually How to use FFT correctly and efficiently in practice What Is FFT? In the time domain, a signal is just a waveform changing over time – all components “stacked together” in one trace. You can see it, but it’s hard to tell which frequencies are inside. FFT (Fast Fourier Transform) decomposes a time-domain signal into a sum of sinusoids at different frequencies. In the frequency domain, the signal is represented by frequency + amplitude + phase. In simple terms: Time domain: how the signal moves over time Frequency domain: what frequency components it contains, which are strongest, and how they relate to each other Historically, Fourier’s key idea (early 19th century) was that a complex periodic function can be expressed as a sum of sines and cosines. This evolved into the continuous-time Fourier transform, mapping signals onto a continuous frequency axis. In the computer age, things changed: engineers work with sampled data and typically only have a finite-length record of N samples. That leads to the DFT (Discrete Fourier Transform), which maps N time samples to N discrete frequency bins. FFT (Fast Fourier Transform) is not a different transform. It is a family of algorithms that compute the exact same DFT much more efficiently: Direct DFT: complexity ~ O(N²) FFT: complexity ~ O(N log N) The output X[k] is identical to the DFT result – FFT just gets there far faster by exploiting symmetry and divide-and-conquer. What FFT Is Good at – and What It Isn’t FFT is very good at: Finding deterministic narrowband components Fundamental tones, harmonics, switching frequencies, whistle tones, speed-related lines Looking at broadband distributions Noise floor, 1/f slopes, in-band power, SNR Characterizing system behavior Transfer functions, resonances / anti-resonances, coherence, delay estimation Serving as the foundation of time–frequency analysis STFT, spectrograms, etc. FFT is not good at (or not sufficient on its own for): Strongly non-stationary signals and “instantaneous frequency” For chirps and rapidly changing content, you need STFT, wavelets, or other time–frequency methods, not a single FFT on a long record Separating two extremely close tones below your frequency resolution If the spacing is smaller than your bin resolution (set by N), no algorithm will magically resolve them Turning short data into “long measurements” Zero padding only interpolates the spectrum visually; it does not add new information Before Using FFT: Key Concepts to Get Right To use FFT well, you need to be confident about a few fundamentals: Sampling rate DFT and its interpretation What you actually plot (magnitude, amplitude, power, PSD) Windowing and spectral leakage Averaging Sampling Rate: How High in Frequency You Can See Before FFT, you already made one crucial decision: sampling. A continuous-time signal x(t) is turned into a discrete sequence x[n]=x(n/fs). The sampling rate fsf_sfs determines the highest frequency you can observe without aliasing: the Nyquist frequency, fs/2. If the analog signal contains energy above fs/2, it does not disappear – it folds back into the band below Nyquist as aliasing. Once aliasing happens, FFT cannot “undo” it; the information is irretrievably mixed. In practice, you must use an anti-alias filter before the ADC (or before any resampling) to suppress components above Nyquist. Example: A 900 Hz sine sampled at fs=1 kHz will appear at 100 Hz in the discrete spectrum – a classic aliasing artifact. DFT Computation and Interpretation Given N samples x[0]..x[N−1], the DFT is defined as: The inverse transform (IDFT) reconstructs the time signal: Intuitively, X[k] tells you how strongly the signal correlates with a complex exponential at that bin’s frequency. The magnitude X[k] indicates “how much” of that frequency component exists The phase encodes time alignment relative to other components What Are You Plotting? Magnitude, Amplitude, Power, PSD From one set of FFT results X[k], you can create many different “spectra” that look similar but represent different physical quantities. This is where confusion between tools and platforms often arises. Common variants include: Magnitude spectrum |X[k]| Units depend on normalization (e.g., “V·samples”) Useful for locating peaks, harmonics, and general spectral shape Amplitude spectrum Properly scaled magnitude, in physical units (e.g. V) Appropriate for reading off sinusoid amplitudes and doing calibrated measurements Power spectrum |X[k]|² Again, scaling dependent; often used for power/energy comparisons when conventions are fixed Power Spectral Density (PSD) Sxx(f) Units like V²/Hz or Pa²/Hz Used for noise analysis, band power, and comparisons across different FFT lengths If you want to compare noise levels across different FFT sizes, windows, or tools, use PSD (or amplitude spectral density). Raw |X| or |X|² values are rarely directly comparable. A Concrete Example: Two Tones in Time and Frequency Imagine a signal consisting of two sinusoids at different frequencies. In the time domain, their sum may look like a “wobbly” waveform. In the frequency domain (FFT/PSD), you will see two distinct narrow peaks at the corresponding frequencies. In OpenTest’s FFT analysis, you can visualise both the spectrum and PSD/ASD side by side, making it easy to: Identify tonal components Inspect noise distribution Compare different operating conditions on the same frequency grid Try it yourself: Download the free OpenTest edition and run an FFT on a simple two-tone signal to see both peaks clearly separated. Window Functions and Spectral Leakage: Cleaning Up Spectra In theory, FFT assumes the sampled block contains an integer number of periods and is then repeated periodically. In reality, the record almost never lines up perfectly with an integer number of cycles. When you repeat that block, you get discontinuities at the boundaries, which causes energy to spread into neighboring bins — this is spectral leakage. To reduce leakage, we typically apply a window function to the time record before doing FFT. A window simultaneously affects: Main lobe width Wider main lobe = peaks get broader → it’s harder to separate close tones Side lobe height Lower side lobes = easier to see small peaks near a large one (better dynamic range) Amplitude/energy scaling Windows change the relationship between a pure tone’s true amplitude and the observed peak, as well as the noise floor level Some practical guidelines: Rectangular window Only use when you can ensure coherent sampling (an integer number of periods in the record) and you want the narrowest possible main lobe Hanning (Hann) window A very robust default choice for general acoustics and vibration work Widely used with Welch/PSD methods Hamming Similar to Hann, with slightly different side-lobe behavior, common in communications Blackman / Blackman–Harris Lower side lobes, useful when you need to see small peaks next to big ones, at the cost of a wider main lobe In OpenTest, you can switch between different window functions in the FFT analysis module and immediately see the impact on peak width, side lobes, and noise floor. Averaging: Making Spectra More Stable For noisy or non-stationary signals, a single FFT can look very “spiky” or unstable. By averaging multiple spectra, you obtain a smoother, more repeatable result. Common averaging types include: Linear averaging A simple arithmetic mean of several FFT results Exponential averaging Recent data gets more weight; good for live monitoring when the spectrum should react but not jump wildly Energy (power) averaging Based on power; ensures power-related quantities remain consistent A good averaging configuration strikes a balance between suppressing random fluctuations and preserving genuine changes in the signal. Where Do We Use FFT in Practice? Audio and Acoustics Typical applications include: Finding feedback frequencies, harmonic distortion, and device noise floors Frequency response (transfer function) measurement Room modes / resonance analysis Spectrograms of speech, music, and equipment noise In audio/acoustics, you must be clear about units and conventions: dB SPL, A-weighting, 1/3-octave bands, etc. FFT is the engine; the reporting convention (reference, weighting, bandwidth) must be clearly defined. Vibration and Rotating Machinery Identifying speed-related peaks (1X, 2X, gear mesh frequencies) Structural resonances and mode behavior under different operating conditions Bearing diagnostics, gear whine, imbalance, misalignment For bearing and gearbox analysis, envelope detection/demodulation is often used: Band-pass filter the signal Demodulate and then perform FFT on the envelope to reveal fault frequencies If the rotational speed is changing, a simple FFT will “smear” peaks. In that case, order tracking or synchronous resampling is more appropriate, turning the axis from “frequency” into “order”. Power Electronics and Power Quality Line frequency harmonics (50/60 Hz and multiples), THD, ripple, switching spikes Pre-compliance EMI checks: spectral lines, noise floor, in-band power In power systems, non-coherent sampling is a common issue: if the record length is not an integer number of mains cycles, leakage affects harmonic accuracy. Solutions include synchronous sampling, integer-cycle windows, or specialized harmonic analyzers. RF and Communications (Baseband View) Modulated signal spectra and spectral masks OFDM and multi-carrier spectral analysis, adjacent channel leakage Here, consistency is paramount: Same units Same bandwidth (RBW) Same window, detector, and averaging style FFT itself is straightforward; turning it into comparable power measurements requires tightly defined settings. Imaging and 2D Filtering 2D FFT extends the same idea to images: Edges correspond to high spatial frequencies; smooth areas to low frequencies Low-pass / high-pass filtering, removal of periodic noise, convolution acceleration in the frequency domain The same periodic extension assumption now applies in 2D: discontinuities at image borders produce strong artifacts in the frequency domain. Padding, mirrored borders, or 2D windows are common ways to mitigate this. Turning FFT into an Everyday Engineering Tool From a mathematical standpoint, FFT is not particularly “lightweight”. But in engineering use, the goal is actually simple: See what’s hidden inside the signal more clearly and much faster. When you understand: What FFT really computes How sampling, windowing, scaling, and averaging affect the result When to use spectra vs PSD, and which settings matter for your use case …then FFT stops being an abstract math topic and becomes a practical, everyday tool for acoustics and vibration work – from R&D and validation all the way to production testing. Download and get started now -> or fill out the form below ↓ to schedule a live demo. Explore more features and application stories at www.opentest.com.

ISO 3744 Sound Power Testing with OpenTest

Under regulations such as the EU Machinery Noise Directive, more and more products—from toys and power tools to IT equipment—are required to declare their sound power level on labels and in documentation, rather than simply claiming they are “quiet enough.” For typical office devices like notebook computers, idle noise is often around 30 dB(A), while full-load operation can approach 40 dB(A). These figures are usually obtained from sound power measurements performed in accordance with ISO 3744 and related standards. Sound Pressure vs. Sound Power A noise source emits sound power, while what we measure with a microphone is sound pressure. Sound pressure varies with room size, reverberation, and microphone distance, whereas sound power is the source’s own “noise energy” and does not change with installation or environment. That makes sound power a better metric for external product noise specification. In simple terms: Sound power is the cause – the energy emitted by the source (unit: W / dB); Sound pressure is the effect – the sound pressure level we hear and measure (unit: Pa / dB). ISO 3744 defines how to do this in an “essentially free field over a reflecting plane”: arrange microphones around the source on an enveloping measurement surface, measure the sound pressure levels on that surface, then apply specified corrections and calculations to obtain stable, comparable sound power levels. Device Under Test: An Everyday Notebook Computer Assume our DUT is a 17-inch office notebook. The goal is to determine its A-weighted sound power level under different operating conditions (idle, office load, full load), in order to: Compare different cooling designs and fan control strategies; Provide standardized data for product documentation or compliance; Supply baseline data for sound quality engineering (for example, whether the fan noise is annoying). The test environment is a semi-anechoic room with a reflecting floor. The notebook is placed on the reflective plane, and multiple microphone positions are arranged around it (using a hemispherical frame or a regular grid). Overall, the setup satisfies ISO 3744 requirements for the measurement surface and environment. Measurement System: SonoDAQ Pro + OpenTest Sound Power Module On the hardware side, we use SonoDAQ Pro together with measurement microphones, arranged around the notebook according to the standard. OpenTest connects to SonoDAQ via the openDAQ protocol. In the channel setup interface, you select the channels to be used and configure parameters such as sensitivity and sampling rate. From Standard to Platform: Why Use OpenTest for Sound Power? OpenTest is CRYSOUND’s next-generation platform for acoustic and vibration testing. It supports three modes—Measure, Analysis, and Sequence—covering both R&D laboratories and repetitive production testing. For sound power applications, OpenTest implements a sound-pressure-based solution fully compliant with ISO 3744 (engineering method), and also covering ISO 3745 (precision method) and ISO 3746 (survey method). You can flexibly select the test grade according to the test environment and accuracy requirements. The platform includes dedicated sound power report templates that generate standards-compliant reports directly, avoiding repeated manual work in Excel. On the hardware side, OpenTest connects to multi-brand DAQ devices via openDAQ, ASIO, WASAPI, and NI-DAQmx, enabling unified management of CRYSOUND SonoDAQ, RME, NI and other systems. From a few channels for verification to large microphone arrays, everything can be handled within a single software platform. Three Steps: Running a Standardized ISO 3744 Sound Power Workflow Step 1: Parameter Setup and Environment Preparation After creating a new project in OpenTest: In the channel setup view, select the microphone channels to be used and configure sensitivity, sampling rate, frequency weighting, and other parameters. Switch to Measure > Sound Power and set the measurement parameters: Test method and measurement-surface-related parameters; Microphone position layout; Measurement time; Other parameters corresponding to ISO 3744. This step effectively turns the standard’s clauses into a reusable OpenTest scenario template. Step 2: Measure Background Noise First, Then Operating Noise According to ISO 3744, you must measure sound pressure levels on the same measurement surface with the device switched off and device running, in order to perform background noise corrections. In OpenTest, this is implemented as two clear operations: Acquire background noiseClick the background-noise acquisition icon in the toolbar. OpenTest records ambient noise for the preset duration.In the survey method, OpenTest updates LAeq for each channel once per second;In the engineering and precision methods, it updates the LAeq of each 1/3-octave band once per second. Acquire operating noiseAfter background acquisition, click the Test icon. OpenTest will:a. Record notebook operating noise for the preset duration;b. Update real-time sound pressure levels once per second;c. Automatically store the run as a data set for later replay and comparison. Step 3: From Multiple Measurements to One Standardized Report After completing multiple operating conditions (for example: idle, typical office work, full-load stress): In the data set view, select the records you want to compare and overlay them to observe sound power differences under different conditions; In the Data Selector, click the save icon to export the corresponding waveform files and CSV data tables for further processing or archiving; Click Report in the toolbar, fill in project and device information, select the data sets to include, adjust charts and tables, and export an Excel report with one click. The report includes measurement conditions, measurement surface, band or A-weighted sound power levels, background corrections, and other key information. It can be used directly for internal review or regulatory/customer submissions, following the same idea as other standardized sound power reporting solutions. From a Single Notebook Test to a Reusable Sound Power Platform Running an ISO 3744 sound power test on a notebook is just one example. More importantly: The standardized OpenTest scenario can be cloned for printers, home appliances, power tools, and many other products; Multi-channel microphone arrays and SonoDAQ hardware can be reused across projects within the same platform; The test workflow and report format are “locked in” by the software, making it easier to hand over, review, and audit across teams. If you are building or upgrading sound power testing capability, consider using ISO 3744 as the backbone and OpenTest as the platform that links environment, acquisition, analysis, and reporting into a repeatable chain—so each test is clearly traceable and more easily transformed from a one-off experiment into a lasting engineering asset. Visit www.opentest.com to learn more about OpenTest features and hardware solutions, or contact the CRYSOUND team by filling out the “Get in touch” form below.

January 13, 2026

ISO 532 & ECMA-74 Sound Quality Measurement with OpenTest

Why Sound Quality Measurements Matter