At Listen, we’re at the forefront of audio measurement research, and we’re always looking to improve on existing audio measurement techniques, even our own! Loose Particle detection has been a valuable production metric for analyzing transient distortion since we launched it in 2004. This new iteration uses our own original research to improve accuracy and reliability, show a clear correlation to audibility, and simplify limit setting. In addition to production line testing of speakers, headphones, drivers and other devices, it is also valuable for automotive Buzz, Squeak and rattle (BSR) measurements and measuring rattling components such as keys and buttons on a variety of devices. All is explained in the short video below.
Transient Distortion Detection Launch Video
Watch our launch video (broadcast date 05/25/2023) for the full details.
Ready to Measure your Transient Distortion?
If you have SoundCheck 21, you already have this new algorithm! Download our free complete end of line test sequence with enhanced Loose Particles to start using it right away.
If you don’t have SoundCheck 21, but have an older version, you can send us your recorded waveforms and we’ll analyze them for you and send you the results. Please contact firstname.lastname@example.org for a test sequence to record the waveforms.
No SoundCheck system at all? No problem! You can send us your speakers and we’ll test them for you. Or we may be able to arrange a system loan. Contact your sales engineer at email@example.com for more information.
Prefer to Read About It?
We know not everyone has 15 mins free to watch a video, although its hard to beat the benefits of a proper demonstration. So here’s a brief summary of the information about this new algorithm that is presented in the video:
What it does
The new algorithm measures transient distortion caused by loose particles that may become trapped in a device during manufacture and create an unpleasant sound when they vibrate in the finished product. This is measured in the time domain rather than the frequency domain as these artifacts appear randomly over time, and not periodically in the same way that harmonic distortion artifacts are presented. This algorithm has a couple of unique features:
1) It measures transient distortion separately from harmonic distortion which gives deeper insight into the failure mode and accelerates troubleshooting, particularly on the production line.
2) It’s easy to correlate with audibility as the algorithm removes the stimulus waveform to allow the user to listen to just the distortion artifacts. As well as enabling the user to truly understand how measured results correlate to listening, this also facilitates limit setting.
3) It’s reliable even in the presence of transient background noise since it relies on a cumulative event count rather than a single event triggering a fail. Limit setting is simple as it is not frequency dependent
- Production line driver test
- Production line finished product test (rattling buttons, keys, grills, and other components)
- Buzz, Squeak and Rattle (BSR) / Impulsive distortion measurements in cars
How it works
Like Listen’s original 2004 transient distortion detection algorithm, enhanced Loose Particles relies on a time domain analysis of the waveform. However, rather than simply filtering and counting transients, it removed the stimulus, performs a time domain analysis of the remaining transients, then applies a unique Prominence calculation to evaluate each transient in the context of the surrounding waveform. A threshold is applied, and the number of transient events above that threshold are counted. The event count indicates whether a device has a transient distortion problem. Transient events caused by speaker manufacturing issues tend to repeat many times as particles bounce around in side the speaker, or components vibrate. In contrast, background noise events occur infrequently during the duration of the measurement. The threshold is set based on audibility, and the Loose Particle count limit can be set according to the environment to fine-tune the algorithm for the specific operating conditions.
For additional background, comparison to other methods, such as Crest Factor Analysis, and live demonstrations, please check out the video above.