Technical Resources
With over 100 combined years of audio measurement experience, our team has created a wealth of technical papers, sequences, articles and other useful information to assist you with your audio test needs. Please browse the collection below, or filter by type of resource.
Practical Measurement of Bluetooth and Lightning Headphones
Author: Daniel Knighten. Reprinted from the July 2017 issue of Voice Coil.
In this article, Dan Knighten discusses how to overcome the challenges of measuring headphones with wireless and digital interfaces such as Bluetooth, Lightning and USB-C to make the same measurements as on conventional wired headphones.
Smarter Measurements for Smart Speakers
Author: Daniel Knighten (Listen, Inc) and Glenn Hess (Indy Acoustic Research). Reprinted from the March 2018 issue of Audio Xpress.
In this article, we describe techniques to characterize the frequency response, output level, and distortion of the device under test to enable direct comparisons between Internet of Things (IoT) smart speakers and conventional speakers.
In-Vehicle Distortion Measurement
Authors: Zarina Bhimani, Steve F. Temme (Listen, Inc.), Patrick Dennis (Nissan Motor Co.). Reprinted from the 2017 Loudspeaker Industry Sourcebook.
Steve Temme and Patrick Dennis discuss their research exploring test methods that will help determine audible distortion and enable manufacturers to test sound equipment after it is installed
Microphone Polar Plot: Substitution Method Using Outline ET250-3D
This sequence measures the directional response of a microphone and graphs the result as a polar plot. A log sweep stimulus is played from 100 Hz to 10 kHz at each angular increment, and the acquired waveform is analyzed using the Time Selective Response algorithm. This method allows the test to be performed in a non-anechoic environment by placing a window around the direct signal, eliminating the influence of reflections. Commands are sent automatically to the Outline turntable via an RS-232 connection, instructing it to move in 10 degree increments after each measurement. The sequence measures the response every 10 degrees from 0 to 180 and mirrors the polar image, which simulates a full 360 degree polar and saves test time. The response at each angular increment is compared against the on-axis response to create a normalized curve. This removes the influence of the device’s frequency response and sensitivity, such that the polar plot only shows the directional response. The final display also contains a graph of the directivity index in decibels versus frequency.
Microphone Polar Plot: Substitution Method Using LinearX LT360 Turntable
This sequence measures the directional response of a microphone and graphs the result as a polar plot. A log sweep stimulus is played from 100 Hz to 10 kHz at each angular increment, and the acquired waveform is analyzed using the Time Selective Response algorithm. This method allows the test to be performed in a non-anechoic environment by placing a window around the direct signal, eliminating the influence of reflections. Commands are sent automatically to the LT360 turntable via an RS-232 connection, instructing it to move in 10 degree increments after each measurement. The sequence measures the response every 10 degrees from 0 to 180 and mirrors the polar image, which simulates a full 360 degree polar and saves test time. The response at each angular increment is compared against the on-axis response to create a normalized curve. This removes the influence of the device’s frequency response and sensitivity, such that the polar plot only shows the directional response. The final display also contains a graph of the directivity index in decibels versus frequency.
Smart Speaker – Embedded Microphone Test Sequence
This sequence demonstrates a method by which SoundCheck can measure the performance of a microphone embedded in a so-called “smart speaker”. This example assumes that the DUT is an Amazon Echo but it can be adapted for use with virtually any other type of smart speaker by substituting the Echo’s voice activation phrase WAV file (“Alexa”) with one specific to the desired make and model.
The sequence begins by playing a voice activation phrase out of a source speaker, prompting the DUT to record both the voice command and the ensuing stepped sine sweep stimulus. A message step then prompts the operator to retrieve this recording from the DUT’s cloud storage system. This is accomplished by playing back the recording from the cloud and capturing it with a Triggered Record step in the SoundCheck test sequence. The Recorded Time Waveform is then windowed (to remove the voice command) and frequency shifted prior to analysis and the result (Frequency Response) is shown on the final display step.
Smart Speaker – Embedded Loudspeaker Test Sequence
This sequence demonstrates a method by which SoundCheck can measure the performance of a loudspeaker embedded in a so-called “smart speaker”. This example assumes that the DUT is an Amazon Echo but it can be adapted for use with virtually any other type of smart speaker by substituting the Echo’s voice activation phrase audio file (“Alexa, play Test Signal One”) with one specific to the desired make and model.
The sequence begins by playing the voice activation phrase out of a source speaker, prompting the DUT to playback the mp3 stimulus file from the cloud, followed by a pause step to account for any activation latency. Following the pause, a triggered record step is used to capture the playback from the DUT. The Recorded Time Waveform is then frequency shifted prior to analysis and the results (Frequency Response, THD and Perceptual Rub & Buzz) are shown on the final display step.
We recommend reading our AES paper on this subject prior to continuing as it contains additional details on the test methods devised for this sequence.
Challenges of IoT Smart Speaker Testing
Quantitatively measuring the audio characteristics of IoT (Internet of Things) smart speakers presents several novel challenges. We discuss overcoming the practical challenges of testing such devices and demonstrate how to measure frequency response, distortion, and other common audio characteristics. In order to make these measurements, several measurement techniques and algorithms are presented that allow us to move past the practical difficulties presented by this class of emerging audio devices. We discuss test equipment requirements, selection of test signals and especially overcoming the challenges around injecting and extracting test signals from the device.
Authors: Glenn Hess (Indy Acoustic Research) and Daniel Knighten (Listen, Inc.)
Presented at the 143rd AES Conference, New York 2017
