Introducing Enhanced Loose Particles (eLP)

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Watch our launch seminar for Listen’s latest algorithm, enhanced Loose Particles (eLP). This new algorithm significantly improves production line transient distortion measurement. It offers high accuracy, easy correlation to human perception, and identifies transient distortion artifacts separately from harmonic distortion for easy troubleshooting. It’s also ideal for identifying rattling buttons in audio devices, and automotive Buzz, Squeak and Rattle (BSR) measurements. This new algorithm is based on original research, and uses techniques not previously used for distortion measurement. Check out this demonstration and detailed explanation of how this algorithm works by Listen president Steve Temme.

Introducing Enhanced Loose Particles

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For all things Enhanced Loose Particles including listening examples, published articles, and details of the eLP methodology, check out our Enhanced Loose Particles page.

Listen’s new enhanced Loose Particle algorithm offers accurate transient distortion measurements, even in the presence of background noise. This algorithm is highly accurate, as well as easy to configure and set limits. Results are easily correlated to audibility by listening to the loose particles in the recorded waveform.

This algorithm uses the same time envelope analysis as our pioneering (2004) Loose Particles algorithm, but rather than filtering and counting pulses, additional analysis is applied to measure the prominence, or impulsiveness, of the detected artifacts. The algorithm calculates the prominence of each loose particle event in the waveform by comparing the magnitude of a peak relative to the surrounding minimums, which reflects the impulsiveness of the artifact more accurately than the absolute magnitude of the peaks.  A user-defined prominence threshold determines the level at which the peak will be counted, and a user-defined count of events over the time window determines the pass/fail threshold.

Objective results obtained by this method are easily correlated to subjective analysis as you can listen to the recorded waveform with the fundamental removed, hearing only the loose particles. This enables the prominence threshold to easily be set based on defect audibility.

This analysis method is inherently reliable in a factory environment as external background noise events typically only occur once or twice during a measurement, whereas many loose particle transients will occur during the same timeframe. The event count is user-determined, and is set according to the background noise in the measurement environment. Prominence threshold and loose particle count are the only parameters that the user needs to define, so limit setting is simple, well correlated to audibility, and can be configured to give reliable results even when background noise is present.