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.

Testing Voice-Controlled & Smartphone Integrated Infotainment Systems

A tutorial and accompanying paper on testing infotainment systems that was presented at the AES Automotive Conference, Sept 11-13, 2019, Neuburg an der Donau, Germany.

Voice-controlled and smartphone integrated vehicle infotainment systems are notoriously complex to test. They have numerous connections from wired to wireless and contain much signal processing, both on the record and on the playback side. This means that their characteristics change according to ‘real world’ conditions of the vehicle’s environment, including cabin acoustics and background noises from road, wind and motors. Furthermore, their multifunctional nature means that there are many aspects of the device that may need to be tested, ranging from voice recognition to music playback and operation as a hands-free telephone. Due to their complex non-linear use cases, these devices often need to be tested at different levels and different environmental conditions.

This tutorial offers practical hands-on advice on how to test such devices, including test configurations, what to measure, the challenges of making open-loop measurements, and how to select a test system.

Download presentation (slides)

Download accompanying paper

 

Paper Introduction

Audio Tests for Infotainment Systems

Infotainment systems have become increasingly challenging to test. They have many possible interfaces; hard-wired or auxiliary input, radio, CD, memory card, hard drive, USB, Bluetooth, smartphone (including Apple CarPlay and Android Auto) and even voice. They contain much signal processing, both on the record side (e.g. beamforming, background noise filtering, voice activity detection, and on the playback side (e.g.loudness, compression, equalization, and active noise cancellation). Some even have wake word detection, e.g. “Hey Siri”, “OK Google”, and “Alexa”. Due to their complex non-linear use cases, these devices often need to be tested at different levels and in different environmental conditions, for example with different background noises and different test signals. To further complicate matters, the test signal may need to be in the cloud to enable playback for testing voice recognition systems. Each manufacturer’s ecosystem is different in how it plays and records.

Smartphone integrated infotainment systems usually require an internet connection with voice services in order to process commands. On the playback side, some enable you to upload your own recordings such as iTunes (although bear in mind that these will probably be compressed). Others require them to be on a media streaming platform such as Spotify. For microphone testing, some systems such as Alexa allow access to recordings made; others do not for security and privacy reasons, which makes microphone testing challenging. Although the actual physical testing setup is very similar from vehicle to vehicle, for each it is necessary to understand how to wirelessly route the signal. Furthermore, each device needs activating with a different wake word, needs different delay compensation, and records for a different amount of time after it hears the wake word. This needs figuring out (largely by trial and error) for each infotainment system that you need to test.

Infotainment System Testing Standards

Although, there are currently no standards for testing infotainment systems with smartphone integration, principles and test configurations can be borrowed from many other audio devices and use existing standards such as IEC for loudspeakers, IEEE/TIA/ITU for speakerphones, and ETSI for background noise. Flexibility of the test system and experience with testing a wide range of acoustic devices is critical to enable a device to be completely characterized. This paper focuses on how to implement basic acoustic tests and some of the more complex real-world tests along with the techniques and standards that may be used.

The rest of the paper covers:

Basic Acoustic Tests
Advanced or ‘Real World’ Testing of Infotainment Systems
Speech Recognition
Background Noise
Voice Quality
Measurement System Requirements

Full Paper

 

More about testing infotainment systems.

Evaluation of Audio Test Methods and Measurements for End-of-Line Automotive Loudspeaker Quality Control

In order to minimize costly warranty repairs, automotive manufacturers impose tight specifications and a “total quality” requirement on their part suppliers. At the same time, they also require low prices. This makes it important for automotive manufacturers to work with automotive loudspeaker suppliers to define reasonable specifications and tolerances, and to understand both how the loudspeaker manufacturers are testing and also how to implement their own measurements for incoming QC purposes.

Specifying and testing automotive loudspeakers can be tricky since loudspeakers are inherently nonlinear, time variant and affected by their working conditions & environment which can be change dramatically and rapidly in a vehicle. This paper examines the loudspeaker characteristics that can be measured, and discusses common pitfalls and how to avoid them on a loudspeaker production line. Several different audio test methods and measurements for end-of-the-line automotive speaker quality control are evaluated, and the most relevant ones identified. Speed, statistics, and full traceability are also discussed.

Authors: Steve Temme, Listen, Inc. and Viktor Dobos, Harman/Becker Automotive Systems Kft.
Presented at the 142nd AES Convention, Berlin, Germany

Full Paper

In-Vehicle Audio System Distortion Audibility versus Level and Its Impact on Perceived Sound Quality

As in-vehicle audio system output level increases, so too does audio distortion. At what level is distortion audible and how is sound quality perceived as level increases? Binaural recordings of musical excerpts played through the in-vehicle audio system at various volume levels were made in the driver’s position. These were adjusted to equal loudness and played through a low distortion reference headphone. Listeners ranked both distortion audibility and perceived sound quality. The distortion at each volume level was also measured objectively using a commercial audio test system. The correlation between perceived sound quality and objective distortion measurements is discussed.

Authors: Steve Temme, Listen, Inc. and Patrick Dennis, Nissan Technical Center North America, Inc.,
Presented at the 141st AES Convention, Los Angeles, CA 2015

Full Paper