Tag Archive for: car audio

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.

Automotive Max SPL (Maximum Sound Pressure Level)

Screenshot showing final display of automotive Max SPL sequence

Final display of automotive Max SPL sequence showing individual Max SPL curves, Max SPL average curve and Max SPL value.

This sequence measures Automotive Max SPL, the Maximum Sound Pressure Level (SPL) of a car infotainment system in the vehicle’s interior. It calculates a single value for Max SPL and displays the Max SPL Spectrum, showing the six individual microphone responses plus the average curve.

The sequence uses a 6 microphone array mounted at either the driver or passenger locations. A 30 second pink noise stimulus having an RMS level of -12 dBFS is played through the infotainment system and captured by SoundCheck’s Multi-channel Real Time Analyzer (RTA). The Multi-channel RTA produces 6 RTA curves which are then power averaged to produce a Max SPL Spectrum. The spectrum is then power summed to produce a single value for Max SPL.

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Practical Testing of Voice Controlled Devices

Author: Steve Temme.  Reprinted from the Jan 2020 issue of AudioXpress.

This article discusses tools and techniques that are available to accurately measure the audio performance of voice-controlled and connected devices under the many various real-world conditions they may be used. It covers basic acoustic measurements such as frequency and distortion response, which have always been carried out on conventional wired systems, and the more complex real-world tests that apply specifically to voice-activated devices, along withthe techniques and standards that may be used.
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The Challenges of Testing Voice-Controlled Audio Systems

Testing voice-controlled audio systems such as smart speakers, hearables, and vehicle infotainment systems is notoriously complex. They have numerous connections from wired to wireless and contain much signal processing, both on the record and the playback side. This means that their characteristics change according to ‘real world’ conditions of the environment that they are used in, such as background noise, playback levels, and room acoustics. 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, operation as a hands-free telephone, and in the case of hearables, hearing assistance. Due to their complex non-linear use cases, these devices often need to be tested at different levels and different environmental conditions. This paper focuses on tools and techniques to accurately measure the audio performance of such devices under the many various real-world conditions in which they are used.

 

语音控制的智能设备(例如智能扬声器、听觉设备和车辆信息娱乐系统)非常难以测试。它们具有从有线到无线的多样连接方式,并且在接收端和重放端使用了诸多信号处理技术。这意味着它们的特性会随着使用环境的“现实世界”条件(例如背景噪声、播放级别和室内声学条件)的不同而变化。 此外,它们的多功能特性意味着可能需要测试该设备的许多方面,包括语音识别、音乐播放、作为免提电话或听觉设备或助听器使用时的性能。由于其复杂的非线性使用情况,这些设备通常需要在不同级别和不同环境条件下进行测试。本文重点介绍在各种实际条件下准确测量此类设备的音频性能的工具和技术。

Author: Steve Temme, Listen, Inc.
Presented at ISEAT 2019, Shenzhen, China.

Full Paper – English Version
Full Paper – Chinese Version

 

Paper Preview: The Challenges of Testing Voice-Controlled Audio Systems

Abstract
Smart devices that are voice-controlled such as smart speakers, hearables, and 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 the playback side. This means that their characteristics change according to ‘real world’ conditions of the environment that they are used in, such as background noise, playback levels, and room acoustics. 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, operation as a hands-free telephone, and in the case of hearables, hearing assistance. Due to their complex non-linear use cases, these devices often need to be tested at different levels and different environmental conditions. This paper focuses on tools and techniques to accurately measure the audio performance of such devices under the many various real-world conditions in which they are used.

Keywords: hearables, automotive infotainment, smart speakers, smartphones, test

Introduction
Smart Devices such as smart speakers, hearables and automotive infotainment systems have become increasingly challenging to test. They have many possible interfaces ranging from hardwired to wireless (Bluetooth, cloud-based), smartphone, voice (“Hey Siri”, “OK Google”, “Alexa”), and in the case of automotive, even USB memory stick and CarPlay/Android Auto. There is usually 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).
This means that their characteristics change according to ‘real world’ conditions such as the physical environment and background noise. 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 or even operation as a hands-free telephone, telephone headset or hearing aid. These devices often need to be tested at different levels and in different environmental conditions, for example different physical setups and with/without background noise, different signals etc.

Although, there are currently no standards for testing most smart devices, principles and test configurations are borrowed from many other audio devices and use existing standards such as IEC and BS EN for loudspeakers and headphones , IEEE for headsets, IEEE/TIA/ITU for telephone test, ANSI and IEC for hearing aid standards , and ETSI for background noise. Flexibility of the test system and experience with testing a wide range of acoustic devices is therefore critical to enable a device to be completely characterized.

This paper explains how to implement both basic acoustic tests and more complex real-world tests along with the techniques and standards that may be used. Most of the tests discussed are relevant to all smart devices including smart speakers, hearables and automotive infotainment, but some hearable-specific additional tests are also detailed. Finally, we present a check list of the test-system functionality you should look for when choosing a system to fully characterize a smart speaker or other smart device.

 

Full Paper – English Version
Full Paper – Chinese Version

 

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

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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

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