Tag Archive for: infotainment

Automotive Max SPL Measurements

Measuring Automotive Max SPL ArticleIn this short article, Steve Temme discusses measurement of automotive Max SPL, and introduces the efforts of the Audio Engineering Society (AES) technical committee working on automotive audio to standardize the way essential attributes of complex automotive audio systems are measured across the industry. He explains why Max SPL measurements are important, defines this measurement, and describes the standardized measurement procedure suggested by the committee. Test configuration and physical setup is discussed, and example results presented.

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Measuring Automotive Max SPL
By Steve Temme Listen, Inc.
I am currently a participant in an Audio Engineering Society (AES) technical committee working group on automotive audio. This diverse group of about a dozen worldwide experts has focused on trying to standardize the way essential attributes of complex automotive audio systems are measured across the industry. Three specific measurements have been our initial focus: Frequency Response, Max SPL, and Impulsive Distortion. The committee’s proposals for measurements were presented for feedback at the AES Fall Online 2021 conference in a session titled “In-Car Acoustic Measurements.”

I presented our work on Max SPL Measurements, Hans Lahti (Harman) presented Frequency Response, and Stefan Irrgan (Klippel) presented Impulsive Distortion; the session was chaired by Jayant Datta. Here, I will describe our proposed method for Max SPL measurements.

Let’s start with why this is important. People need to be able to compare how loud an infotainment system can play in a car— manufacturers like to quote this in specifications, and consumers enjoy bragging rights about the sound level of their car stereo. Max SPL is defined as the maximum sound pressure level (SPL) that a car’s infotainment system can reproduce inside the cabin with the windows, sunroof, and convertible top closed. There are many ways this can be measured, but to keep it simple, two different measurements are recommended—overall Max SPL and Max SPL Spectrum regardless of distortion level. The reason we don’t take into account distortion when we measure the Max SPL is because it is difficult to characterize distortion in a modern-day infotainment system—these devices frequently contain much signal processing, and this makes them unsuitable for playing back the sine wave stimuli that are typically used for harmonic distortion measurements.

First, let’s examine the physical test setup. Our proposed test configuration replicates the position of an average person’s head in the driver’s seat using a precisely and specifically positioned six- microphone array in the driver’s seat. The height and the angle of the seat, the positioning of the microphones with respect to the seat, and the height and the angle of the microphones are clearly defined to ensure standardized measurements across all vehicles.

The sound system settings on the head unit—the tone control and fader—are set to the factory default setting; in most cases this is neutral or flat with no equalization. The head unit’s volume control is set to its maximum level using the volume control knob or digital user interface equivalent (e.g., volume level slider). Overall Max SPL can be measured using a microphone array with the six microphone signals power averaged by analog or digital means and connected to either a conventional or software-based sound level meter that can measure true RMS and be C-weighted, as described in the IEC-61672 standard. However, if a software-based system is used for measuring the Max SPL Spectrum, it is simpler to also measure the overall Max SPL through the software. Figure 1 shows a test configuration that makes both measurements simultaneously using SoundCheck software, and an AmpConnect 621 audio interface.

For both the overall Max SPL and Max SPL Spectrum measurements, a broadband (20Hz to 20kHz) monophonic pink noise stimulus is used. It has a crest factor of 15dB and is played for 30 seconds to make sure the system can sustain that level continuously. This is played at maximum volume to ensure the system is tested at the loudest signal the car will play. The sound source may come from any source—a memory stick, a CD, or Bluetooth from a smartphone or auxiliary line in. The average SPL in dB(C) is measured for 30 seconds. This is called a Leq measurement, and it takes the spatial average of the six-microphone array, power averaged, to get the overall Max SPL level (Figure 2).

The Max SPL Spectrum is measured using a real-time analyzer set to 1/12 octave resolution, 30 second linear averaging time and no waiting. This enables us to measure the level versus frequency irrespective of the human ear’s perception. The Max SPL is recorded at each microphone simultaneously from 20Hz to 20kHz and the power average calculated (Figure 2).

Listen offers a pre-written SoundCheck test sequence that measures both the Max SPL Spectrum and a single, power averaged value for Max SPL in line with the working group’s proposed guidelines. This enables consumers and manufacturers to measure the maximum overall SPL and maximum SPL versus frequency that a car’s infotainment system can reproduce inside its cabin. The sequence uses the method and test configuration with a six-microphone array in either the driver or passenger seats. It takes advantage of Listen’s 6-in, 2-out AmpConnect 621 audio interface, which seamlessly integrates with the software-based multichannel analyzer to measure, display, and average the results from the six microphones in real time, and power average them to calculate Max SPL. This sequence may be downloaded free of charge from Listen’s website. More details about these measurements, and the other measurement proposals developed by the technical committee, will be presented at the 2022 AES International Conference on Automotive Audio, June 8-10, in Dearborn, MI.

 

Further information on the AES Technical Committee on Automotive Audio, including a link to the working group’s draft white paper on can be found here: https://www.aes.org/technical/aa/

More about measuring automotive Max SPL.

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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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.

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.

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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

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More about testing infotainment systems.