Tag Archive for: perceptual Rub & Buzz

Enhanced Perceptual Rub & Buzz Measurement for Testing Automotive Loudspeakers

Loudspeaker Rub & Buzz faults are a problem for automotive manufacturers as they sound harsh and immediately give the perception of poor quality. There are two places such faults can occur – during speaker manufacturing and installation of the speaker in the car. A buzzing loudspeaker in a car is disappointing to a customer and is costly to replace. It is also challenging for a service center to determine exactly where the buzzing is coming from and whether it is caused by a faulty loudspeaker or bad installation. Perceptual distortion measurements are often considered the holy grail of end-of-line testing because rejecting speakers with only audible faults increases yield. Although such measurements have been around since 2011, production line adoption has been slow because until now, sensitivity to background noise has made limit-setting challenging. In this paper, a new algorithm is introduced that uses advanced technology to reduce the impact of background noise on the measurement and offer more repeatable results. This facilitates limit setting on the production line and makes it a truly viable production line metric for increasing yield. This same metric may also be used for end-of-line automotive quality control tests. Results from various algorithms will be shown, and their correlation to subjective and other non-perceptual distortion metrics explained.

Author: Steve Temme, Listen, Inc.
Presented at 2022 AES Automotive Conference, Dearborn, MI

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Introduction

The automotive industry’s stringent quality expectations make end-of-line quality testing on automotive speakers and drivers absolutely critical. End-of-line tests typically measure a range of parameters including frequency response, THD, and polarity. Manufacturing-introduced defects such as Rub & Buzz and Loose Particles are also measured. Reliable, automated testing has been available for decades now, and most large manufacturers rely on these software-based systems for identification and rejection of defective products. While these tests do an excellent job of identifying defective units, there is always a certain level of false rejection where units with some distortion fail even though it is completely inaudible to the human ear. From a manufacturing perspective, higher yields and therefore greater profitability is always desirable.

Perceptual Distortion Measurements

This has driven the development of perceptual distortion measurements – automated measurements that replicate the human hearing to detect only audible distortion defects. Such metrics increase production line yield by passing products with inaudible distortion, as the product will still sound exactly as the manufacturer intended. Perceptual methods are very simple to configure for production line use. Since they return a result in Phons, an absolute measurement that can be easily correlated to the listener’s threshold of hearing, the operator can set a fixed limit across the board, regardless of product. Naturally, the price point and quality expectations for the product may influence the level of distortion that is deemed acceptable.

Perceptual Distortion Algorithms

Our algorithm, introduced in 2011, was the first commercial perceptual distortion metric, although in the past couple of years, other test system manufacturers have also started to offer perceptual distortion tests. It offers excellent correlation with human hearing and performs well in laboratory tests. However, like the human ear, repeatability decreases in the presence of background noise. This is not a failure of the algorithm as such, but an indication that the algorithm performs just like a human listener; when background noise is high, audible distortion is masked. This limitation restricts the value of such algorithms on the production line, as with today’s high-volume manufacturing, there is only time for one fast test sweep. If this sweep gets a different result under changing background noise conditions, limit setting becomes challenging, and repeatability and reliability is decreased. Similar algorithms from other test system manufacturers also suffer from the same problems.

New Perceptual Distortion Algorithm Development

This paper details efforts to create an algorithm that hears like a human in quiet conditions, e.g. in a living room or passenger automotive cabin, under the less-than-perfect conditions of a manufacturing environment where considerable and varying background noise may be present. In other words, a perceptual model that is more independent and reliable than the human ear when it comes to noisy environments. The resulting new algorithm overcomes these limitations to offer repeatable end-of-line test results, even in noisy environments. It incorporates noise reduction techniques and enhanced perceptual filters to overcome the reliability and high frequency masking issues of earlier versions. In short, the algorithm offers the performance of an ‘enhanced’ human ear – it detects distortion like an ear in a quiet environment, even when there is background noise. This makes it a viable solution for production line use.

In this paper we explain how the algorithm works, demonstrate how the results compare with earlier perceptual algorithms and show its correlation with human hearing and conventional distortion algorithms. We also compare its performance in the presence of background noise to other perceptual algorithms by adding recorded factory background noise to the signal before passing it through the algorithms.

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More about Listen’s enhanced Perceptual Rub & Buzz algorithm

More about in-car measurement of  impulsive distortion / Buzz, Squeak and Rattle.

End of Line Distortion Measurements

Steve Temme discusses the importance of detecting manufacturing-induced defects such as Rub & Buzz and Loose Particles during end-of-line testing, and explains the various algorithms that are used. He compares conventional and perceptual metrics for the measurement of Rub & Buzz, including Listen’s new enhanced Perceptual Rub & Buzz algorithm, and discusses why it can be beneficial to use both conventional and perceptual measurements in tandem.

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SoundCheck 20 Released: ePRB, Features for communications testing, POLQA, Multichannel enhancements and more

SoundCheck 20, Listen’s flagship audio analysis software, introduces a new state-of-the-art perceptual distortion algorithm for end-of-line testing – Enhanced Perceptual Rub & Buzz. It also includes a host of new features for multichannel and communications testing, increasing its capabilities for audio measurement of smart speakers, infotainment systems, headphones and hearables, and other voice-activated and multichannel devices.

The new enhanced Rub & Buzz algorithm is a reliable and repeatable perceptual method for detecting audible Rub & Buzz faults on the production line. This offers manufactures the option to increase yield by rejecting only those devices with audible distortion defects. Based on the sound psychoacoustic principles used in Listen’s industry-standard 2011 perceptual algorithm, new research offers refinements that improve listener correlation and new, patent-pending, noise reduction technology offers unrivaled repeatability and reliability for end-of-line use. In fact,this algorithm performs like a human ear under normal listening conditions with the background noise of a manufacturing environment! SoundCheck measures perceptual Rub & Buzz using the same stepped sine wave, simultaneously with other end-of-line measurements, enabling this valuable analysis to be added to production line tests with no extension in test time.

Two of SoundCheck’s virtual instruments, the multichannel RTA and the Signal Generator, have been significantly upgraded. The multichannel Real Time Analyzer now includes expanded functionality for real time observation of audio signals. It displays multiple channels simultaneously, and offers real time calculation and display of channel, subtraction, maximum, minimum and power average. Applications for this new feature include real-time observation of active noise cancellation, automotive industry standard 6-microphone array infotainment measurements, validating headphone seal on a test head prior to measurement by comparing left and right earphone response, and more. RTA curves are now seamlessly integrated with SoundCheck’s standard graphs and can be dragged and dropped into any graph for easy comparison to limits and reference curves. This is useful for quick comparisons to reference standards or golden units, tuning automotive infotainment systems, and more.

An overhauled signal generator brings additional functionality and simplified operation, particularly when using wav files. In addition to standard audio stimuli (sine, white noise, pink noise, etc.) with user-selected sampling rates and resolution, it can play any wav file, any memory list file, and any complex waveform created by the stimulus editor. The level can be referenced to Peak, dB or RMS. The signal can be equalized in real time, and custom EQ curves applied, which is useful when using a non-flat source such as a mouth simulator. A specific portion of a waveform can be selected to play, either by selecting a start and stop time in seconds, or by examining the waveform, and signals can play for a fixed duration, a fixed number of times, or in a continuous loop. Outputs from 2 or more signal generators can be mixed on the same channel, and waveforms from multiple signal generators can be synchronized, or delayed for phase control during playback.

The optional POLQA (Perceptual Objective Listening Quality Analysis) module brings Opticom’s POLQA 3 algorithm, implementing the ITU-T P.863 standard, right into SoundCheck, where it can be used for perceptual measurements of speech degradation in communications applications ranging from telephones to smart devices. Used just like any other analysis step in a sequence, this is a fast and cost-effective alternative to a panel of human listeners. In addition to speech degradation measurements, it may also be used to assess the impact of noise reduction algorithms, evaluate bluetooth degradation due to packet loss, or to analyze distortions introduced into the audio path.

Several new features expand the software’s post-processing capabilities for testing communications devices. These include batch processing of external files, a silence stimulus step, and RMS level versus time and histogram post-processing steps. A pre-written test sequence for categorizing Doubletalk performance to two international standards, ITU-T P.502 and ETSI TS 126 132 (part #3114), from communications expert John Bareham, is also available. Other enhancements include a stimulus editor upgrade, the ability to export multiple SoundCheck recorded waveforms to a single multichannel wav file and a hardware reset option for quickly changing audio interfaces or restoring hardware to a known configuration.

Measuring Max SPL versus Frequency

This sequence measures the Max SPL of a transducer versus frequency that a device can play back with acceptable distortion. It is particularly valuable for designers using DSP algorithms to optimize the performance of their speakers.

It characterizes the Max SPL of a transducer by setting limits on specific metrics (THD, Rub & Buzz, Perceptual Rub & Buzz, Input Voltage and Compression) and then driving the transducer at a series of standard ISO frequencies, increasing the stimulus level until the one of the limits is surpassed. The sequence begins by measuring the frequency response and impedance of the DUT. The user is asked if they wish to use the -3dB from resonance frequency as the test Start Frequency or manually enter another value. The user is then prompted to enter a Stop Frequency, initial test level and limit values for the metrics of interest. The sequence then plays the stimulus Start Frequency in a loop, increasing the level +3dB with each loop iteration until one of the limits is exceeded.  The stimulus level is then adjusted -3dB and the sequence continues to a second loop which increases the stimulus level +0.5 dB with each loop iteration until the limit is exceeded. At this point, the limit results are saved to an Excel file, the stimulus frequency is incremented by a constant multiplication step and the process is repeated until the Stop Frequency is achieved. Every time the main loop is completed, the individual SPL and Stimulus Level x-y pairs are concatenated to master curves. At the end of the sequence, the Max SPL and Stimulus Level curves are autosaved in .dat format.

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Measurement of Harmonic Distortion Audibility Using A Simplified Psychoacoustic Model

A perceptual method is proposed for measuring harmonic distortion audibility. This method is similar to the CLEAR (Cepstral Loudness Enhanced Algorithm for Rub & buzz) algorithm previously proposed by the authors as a means of detecting audible Rub & Buzz which is an extreme type of distortion[1,2]. Both methods are based on the Perceptual Evaluation of Audio Quality (PEAQ) standard[3]. In the present work, in order to estimate the audibility of regular harmonic distortion, additional psychoacoustic variables are added to the CLEAR algorithm. These variables are then combined using an artificial neural network approach to derive a metric that is indicative of the overall audible harmonic distortion. Experimental results on headphones are presented to justify the accuracy of the model.

Authors: Steve Temme, Pascal Brunet and Parastoo Qarabaqi
Presented at the 133rd AES Convention, San Francisco, 2012

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