Enhanced Perceptual Rub & Buzz (ePRB)

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

Listen’s new patent pending enhanced Perceptual Rub & Buzz (ePRB) 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.

The new ePRB algorithm builds on our pioneering 2011 CLEAR algorithm with new, original research into noise reduction and perceptual modeling. This has resulted in optimized perceptual filters for industry-leading correlation with human hearing, and excellent immunity to background noise for the repeatability needed for successful factory deployment.

Benefits

• Increased yield due to rejection of only devices with audible distortion
• Significantly better noise-immunity, therefore better repeatability than other perceptual distortion algorithms
• Excellent correlation with human perception and conventional Rub & Buzz measurements.
• No increase in test time as it is measured simultaneously with other end-of-line measurements using the same test signal
• Simple limit setting

Applications

While perceptual distortion can be used in a laboratory setting, its main application is end-of-line test since most Rub & Buzz defects are introduced during the manufacturing process. It uses the same test signal as other end-of-line measurements, to  make perceptual measurements simultaneously with other metrics such as frequency response, THD, polarity and normalized Rub & Buzz, with no increase in test time. Configuration is simple – just check the box in the analysis step and enter the desired distortion loudness limit. It works well with speakers, microspeakers, headphones, and other consumer electronics containing transducers such as laptops, smart speakers and more.

ePRB Methodology

This schematic explains the analysis used in the algorithm. The response spectrum is first processed through a noise reduction filter, then two separate mathematical analyses are performed.

One analysis models the frequency response of the ear, based on the psychoacoustic principles outlined in the ITU PEAQ standard for CODEC sound quality evaluation. First, auditory filter bands convert the FFT spectrum to a Bark scale to replicate ear filtering. Next, an ear weighting filter compensates for the transfer function of the outer to inner ear, and finally, the internal noise of the ear is added. A mathematical representation of auditory masking curves (frequency spreading function) is then applied to mimic the psychoacoustic filters of the ear. The fundamental and its masking effects are removed to give the distortion of the speaker, and this is summed over the frequency range to give the perceptual partial loudness.

On the other branch of the algorithm the harmonic structure of the response is quantified using the power cepstrum (a cepstrum is a spectrum of a log spectrum). A strong and extended harmonic structure is a signature of Rub & Buzz.

In the final step of the algorithm, the result of the harmonic analysis is combined with the perceptual distortion to return a harmonically weighted value for Rub & Buzz loudness in Phons. This single-number metric simplifies limit setting.

Performance

Comparison with other perceptual Algorithms

Repeatability of the new ePRB algorithm is significantly better than other perceptual algorithms, making it more reliable for end-of-line test. This is demonstrated by measuring three speakers, one good, one with bad distortion and one with borderline distortion 10 times each and comparing the results. It is clearly seen in the graphs below that Listen’s new ePRB algorithm (left) exhibits significantly greater repeatability than other perceptual algorithms (below).

Comparison of new ePRB with Listen’s 2011 Algorithm

Here we show the enhanced perceptual Rub & Buzz compared to the older algorithm. Note also the enhancement in the ear’s sensitive 1-5KHz range for optimized correlation with listener perception.

Performance with Background Noise

Here we compare results from the two algorithms in a quiet room and with factory noise. It is clear that with the old (2011) algorithm (right), the perceptual distortion is considerably lower in the presence of background noise than in a quiet room, making it harder to detect reliably. In the new ePRB algorithm (left) it is seen that the algorithm provides the same results with background noise as it does in a quiet room.