Tag Archive for: enhanced

Introducing Enhanced Loose Particles (eLP)

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

Read on to learn more

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.

100 Things #24: ePRB (Perceptual Rub & Buzz Measurements)

Not all distortion is audible. Perceptual Rub & Buzz measurements identify only those devices with audible Rub & Buzz defects, rather than all Rub & Buzz defects. Basing end-of-line pass/fail decisions on this metric increases yield while maintaining customer satisfaction.

ePRB (Perceptual Rub & Buzz Measurements)

Learn more about enhanced Perceptual Rub & Buzz

Our full ePRB launch seminar goes in-depth with algorithm explanation, testing demonstrations, use cases, and more.

For all things Enhanced Perceptual Rub & Buzz including listening examples, published articles, and a sequence to try ePRB for yourself, check out our Enhanced Perceptual Rub & Buzz page.

Video Script:

While conventional Rub & Buzz measurements identify faulty products and provide valuable information about the health of your production line, they can sometimes result in devices with inaudible distortion artifacts being rejected. To maximize yields, perceptual distortion metrics enable the rejection of only devices that actually sound bad.

SoundCheck is the only audio test system with a  perceptual Rub & Buzz metric that offers excellent correlation with subjective perception and sufficient noise immunity for production line use. 

We led the way with research in this area here at Listen. In 2011, we launched the first Rub & Buzz algorithm built on a model of human hearing . This original model was well received due to its excellent correlation with subjective listening tests. However, like the human ear, performance was less repeatable in the presence of background noise. Many hours of original research over the past few years have resulted in our new Enhanced Perceptual Rub & Buzz algorithm. This  combines our original methods with proprietary noise suppression and a refined perceptual model to exceed the performance of the human ear. In other words, it replicates the hearing characteristics of a human ear in a quiet environment, even in the presence of background noise.

This means that it offers the excellent noise immunity needed for a wide range of manufacturing environments.

Let me show you what I mean.

These graphs show 3 speakers, a good, bad and borderline loudspeaker, each measured 10 times using 3 different perceptual Rub & Buzz algorithms; our original 2011 version, our new ePRB algorithm, and a perceptual algorithm from another audio measurement company. You can see that while all three indicate the relative magnitude of the distortion, in both our original algorithm and the ‘other’ algorithm, the variation in the repeated measurements for each speaker is inconsistent enough that it would be problematic on the production line. Our new algorithm, the middle graph, clearly shows much greater repeatability between measurements. This offers a high level of confidence in the results and makes it easier to set limits.

Correlation to subjective tests is also improved through the use of more comprehensive masking curves that include additional factors to more  accurately replicate the human ear’s behavior and reveal increased detail in the ear’s highly sensitive 500Hz – 2kHz range. 

Perceptual distortion metrics are a valuable end-of-line test addition as they  increase yield by passing products with inaudible distortion. That said, in most cases, it is desirable to also measure normalized Rub & Buzz and Loose particles, as these are convenient ways of monitoring your production line for early warning of any problems that could eventually lead to a returned product from a customer.

All three distortion measurements, along with a whole host of other end-of-line parameters can be made in SoundCheck simultaneously, using the same stepped sine sweep stimulus signal. In other words, there is no increase in test time when you add perceptual metrics to your end-of-line test. Check out our website for more detailed information, demo sequences to try this out, and more.

Introducing Enhanced Perceptual Rub & Buzz (ePRB)

The new Enhanced Perceptual Rub & Buzz (ePRB) algorithm increases production line yield by rejecting only those speakers with audible Rub & Buzz defects. It offers excellent correlation with human perception, unmatched repeatability and reliability and is fast, adding no addition time to end of line tests. Watch the launch video to learn more about this ground-breaking perceptual distortion algorithm.

Introducing Enhanced Perceptual Rub & Buzz

Read on to learn more

For all things Enhanced Perceptual Rub & Buzz including listening examples, published articles, and a sequence to try ePRB for yourself, check out our Enhanced Perceptual Rub & Buzz page.

Not all distortion is audible. Perceptual Rub & Buzz measurements identify only those devices with audible Rub & Buzz defects, rather than all Rub & Buzz defects. Basing end-of-line pass/fail decisions on this metric increases yield while maintaining customer satisfaction.

Listen’s new enhanced Perceptual Rub & Buzz algorithm is the first to perform better than the human ear. Its proprietary noise reduction technology and advanced perceptual algorithms make its performance in a noisy factory environment comparable to the performance of a human ear under normal listening conditions. In other words, it is the only perceptual Rub & Buzz metric that accurately correlates real-world end-of-line results with listener perception.

Furthermore, it is highly repeatable, simple to configure and set limits, and can be simultaneously implemented with other end-of-line tests with no increase in overall test time.