When we discuss what makes a headphone or earphone sound “good” and “bad”, we inevitably need to ask the question, well how should a headphone&nbsp;sound?Is there an objective way to answer this? Or does it all depend on personal&nbsp;preference?The truth, as so often, is somewhere in the&nbsp;middle.The first question we need to answer is: “what should a speaker sound&nbsp;like?”&nbsp;The short answer is (after decades of&nbsp;research):<ol><li>A speaker should produce a flat frequency response in an anechoic room.&nbsp;</li><li>The loudspeaker should be designed such that its directivity (how the sound is radiated to directions other than to the front) makes it so that when the same speaker is placed in a “normal” (slightly reverberant) room, the frequency response will be a little tilted – about 4 dB more bass, and about 2 dB less treble.</li></ol>The debate about this is&nbsp;basically over, the&nbsp;<a href="https://books.google.at/books/about/Sound_Reproduction.html?id=sGmz0yONYFcC&redir_esc=y" target="_blank">question has been answered</a>, and indeed, virtually all “good” speakers show this behavior (flat on-axis, controlled sound power&nbsp;output).<figure><img data-fr-image-pasted="true" src="https://www.usound.com/wp-content/uploads/2021/04/room-target-curves-1024x683.png" alt="" srcset="https://www.usound.com/wp-content/uploads/2021/04/room-target-curves-1024x683.png 1024w, https://www.usound.com/wp-content/uploads/2021/04/room-target-curves-300x200.png 300w, https://www.usound.com/wp-content/uploads/2021/04/room-target-curves-768x512.png 768w, https://www.usound.com/wp-content/uploads/2021/04/room-target-curves.png 1200w" sizes="(max-width: 1024px) 100vw, 1024px" class="fr-fic fr-dii" style="height: 480px;"></figure>And since recording studios use good speakers (studio monitors) to record, monitor, and mix the music that consumers listen to later, it makes intuitive sense to listen to the music on similarly performing speakers – because that is what the music is supposed to sound like, this is what the artist and recording engineers decided “sounded good”. So the&nbsp;target for speakers is Flat on-axis, controlled sound power output (smooth&nbsp;directivity).Now, the same question can be asked for headphones: “what should a headphone sound like?” (in terms of: What is the ideal frequency response of a headphone”), and the short answer is: “it’s not that&nbsp;simple”.The answer is simple for speakers (not that simple really, but it has been answered), but for headphones, it is much more&nbsp;difficult.The first difficulty is “how do you measure it?”. It’s easy with speakers – put a calibrated microphone at a standardized distance, in a standardized room (e.g. anechoic). With headphones, this isn’t possible, because much of the sound depends on the shape of the head. The general consensus is to measure headphones on artificial heads, with artificial ears and artificial ear canals. The problem with this is, that head shape, ear shape, and ear canal has a significant influence on the acoustics, most prominently a 10-20 dB boost in the upper midrange / treble part of the spectrum, depending on the angle of&nbsp;incidence:<figure><img data-fr-image-pasted="true" src="https://www.usound.com/wp-content/uploads/2021/04/ear-resonance-1024x683.png" alt="Influences on measured ear resonance" srcset="https://www.usound.com/wp-content/uploads/2021/04/ear-resonance-1024x683.png 1024w, https://www.usound.com/wp-content/uploads/2021/04/ear-resonance-300x200.png 300w, https://www.usound.com/wp-content/uploads/2021/04/ear-resonance-768x512.png 768w, https://www.usound.com/wp-content/uploads/2021/04/ear-resonance.png 1200w" sizes="(max-width: 1024px) 100vw, 1024px" class="fr-fic fr-dii" style="height: 480px;"></figure>The important thing is: We “hear” this boost even when listening to speakers – because our ears are&nbsp;always there. When the artificial head measurement shows a high boost at 3 kHz, this sounds “flat, linear” to us, because this is what our ears hear. But how should this boost look exactly? What is the&nbsp;target frequency&nbsp;response?Enter a scientist named Sean Olive. His hypothesis was that the best way to come up with a target response for headphones was to place a pair of good speakers in a “regular” listening room similar to the control rooms of recording and mixing studios, and measuring the frequency response with an artificial head setup. Harman’s&nbsp;<a href="ttps://www.aes.org/e-lib/browse.cfm?elib=14873" target="_blank">reference listening room</a> is neither fully reverberant nor fully anechoic, it features a reverberation time of about 0.4 seconds, very similar to what professional recording and mixing studios&nbsp;use.Now if we measure a headphone using that same artificial head and the headphone was to have the same frequency response that we previously measured in the room, then this frequency response would be ideal, or so Sean Olive proposed. And further research proved that&nbsp;<a href="https://www.aes.org/e-lib/browse.cfm?elib=16768" target="_blank">he was right</a>, the majority of both trained and untrained listeners prefer this target curve over any other target&nbsp;curve.This allows us to escape the circle of confusion, and get closer to the ideal of hearing the same thing the artists, producers and engineers heard when creating the music that we listen to. But the topic does not end&nbsp;here!It’s important to understand that the result of this research is – contrary to popular belief – is not a single fixed frequency response curve that needs to be followed to the letter. In fact, the research confirmed what listeners had suspected for quite some time: The preferred amount of bass varies from person to&nbsp;person.<figure><img data-fr-image-pasted="true" src="https://www.usound.com/wp-content/uploads/2021/04/bass-variation2-1024x683.png" alt="Preferred bass-response on in-ear headphones" srcset="https://www.usound.com/wp-content/uploads/2021/04/bass-variation2-1024x683.png 1024w, https://www.usound.com/wp-content/uploads/2021/04/bass-variation2-300x200.png 300w, https://www.usound.com/wp-content/uploads/2021/04/bass-variation2-768x512.png 768w, https://www.usound.com/wp-content/uploads/2021/04/bass-variation2.png 1200w" sizes="(max-width: 1024px) 100vw, 1024px" class="fr-fic fr-dii" style="height: 480px;"></figure>Additional research performed by&nbsp;<a href="https://iem.kug.ac.at/institut-fuer-elektronische-musik-und-akustik-iem.html" target="_blank">USound and IEM</a> showed that in the presence of background noise (such as distant traffic), listeners tend to prefer more bass and slightly less treble compared to the original Harman&nbsp;Target.For this reason, USound’s USOUND1V1 reference target was tuned by expert listeners to contain more bass energy below 300 Hz, at the same time exhibiting a more smooth and timbrally accurate reproduction of the upper midrange and treble response. This results in a warm and punchy sound without sacrificing clarity, which maintains the original timbre of&nbsp;voices.<figure><img data-fr-image-pasted="true" src="https://www.usound.com/wp-content/uploads/2021/04/target-curve-comparison2-1-1024x683.png" alt="Comparison of target frequency response curve." srcset="https://www.usound.com/wp-content/uploads/2021/04/target-curve-comparison2-1-1024x683.png 1024w, https://www.usound.com/wp-content/uploads/2021/04/target-curve-comparison2-1-300x200.png 300w, https://www.usound.com/wp-content/uploads/2021/04/target-curve-comparison2-1-768x512.png 768w, https://www.usound.com/wp-content/uploads/2021/04/target-curve-comparison2-1.png 1200w" sizes="(max-width: 1024px) 100vw, 1024px" class="fr-fic fr-dii" style="height: 480px;"></figure>The predictable and&nbsp;<a href="https://www.usound.com/usounds-mems-speakers-extreme-bandwidth-technology/" target="_blank">highly precise performance of MEMS loudspeakers</a> enables more accurate tuning with digital filters. The precision of the MEMS actuators makes them the ideal transducer for an acoustically optimized system and allows using more natural-sounding filters to achieve the target sound.

Talking about target curves

How earphones should sound

This article was co-authored by Raj Senguttuvan and Vikram Shrivastava.&nbsp;&nbsp;<h1 dir="ltr">Introduction</h1>The last decade, but particularly 2020, saw a massive increase in video calling and engagement with voice-enabled devices primarily through the work-from-home trend created by the pandemic. However, our use of video calls isn&rsquo;t restricted to just work Zoom meetings. We are now using video calling for teaching, exercise classes, to experience live music, as a tool for interacting at conferences, and <a href="https://www.eatwith.com/" target="_blank">even to go to restaurants.</a> This virtual engagement occurs via laptops, smartphones, tablets, and other IoT devices such as Amazon Echo Show, Facebook Portal, Peloton, <a href="https://imp.i277339.net/c/1141873/823006/11505?u=https%3A%2F%2Ftempo.fit%2Fpdp%2Ftempo-studio&subId2=[]st[p]cjv2u8o050017o5yeyn1o93c5[i]H2ac18[t]w[r]google.com[d]D[z]m" target="_blank">Tempo Studio</a>, etc. We have also embraced the use of voice-controlled devices such as home assistants and wireless earphones, and other smart home applications.&nbsp;The core point that limits enjoyable and engaging interactive audio, video call, or home assistant experiences is consistent sound quality in the presence of noise and other distractors. The ability of your device to intelligently manage sound is what makes or breaks your ability to communicate.&nbsp;<h2 dir="ltr">Increasing audio expectations</h2>Dubbed &lsquo;smart sound&rsquo; by product makers, audio intelligence is the ability of a device to process sound to deliver the best user experience. As the increase in voice-led device use for communication, entertainment, and health management increases, so does the demand for a seamless, low-barrier experience with increased functionality. Users now expect devices to understand more than simple wake or keywords (such as Alexa) and seek the ability to move between devices and applications with superior sound quality to enable an immersive and seamless experience, whether for professional conferencing or personal entertainment.&nbsp;Devices should be able to combine your voice and/or voice command with your personal preferences and environmental data to adapt sound processing to your specific context. This is known as contextual awareness.&nbsp;<h1 dir="ltr">What is Contextual Awareness?</h1>Contextual-aware devices combine user-specific information, such as location, preferences, and ambient sensor data, to better understand what the user is asking for and more accurately execute functions in response to a certain command or trigger.&nbsp;Always-listening devices use signal processing technology combined with <a data-lf-fd-inspected-kn9eq4roawkarlvp="true" href="https://mediatum.ub.tum.de/doc/1463108/document.pdf" rel="noopener noreferrer" target="_blank">machine learning</a> to differentiate between sound types such as natural sounds, voices, background interference, etc. These sounds are often divided into &ldquo;scenes&#39;&#39; and &ldquo;events&rdquo;. A scene is the users setting such as a noisy airport terminal or a quiet working space. The events are someone speaking, a glass breaking, or a dog barking. A context-aware device can process these groups of sounds to ensure the intent of the action, whether it is a video call or a voice command, and is processed for optimal experience.&nbsp; <div class="fr-img-space-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjE0NzkxMjg1ODE2LUlNQUdFIDQ2LTAxLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 788px;" class="fr-fic fr-dib">Figure 1: Context-aware system schematic.&nbsp;Dedicated audio edge processors with a focus on audio fidelity and with machine learning optimized cores are the key to contextually aware and high-quality audio communication devices. These processors can deliver enough compute power to process complex audio using just a small percentage of the energy of a generic Digital Signal Processing (DSP) implementation. These audio edge processors use ML algorithms for superior sound processing in addition to providing a &nbsp;more contextual audio experience.&nbsp;</div> <h2 dir="ltr">&ldquo;I think you are on mute&rdquo;</h2>Video conference calls have been a focal point in 2020 and continue to dominate work and social lives in 2021. The gaps in technology are revealed quickly when devices, people, and environments fail to keep up with the demands of the calls - to the point where the phrase &lsquo;I think you&#39;re on mute&rsquo; was dubbed the<a href="https://www.humanresourcesonline.net/the-most-used-business-phrases-of-2020-i-think-you-re-on-mute" rel="noopener noreferrer" target="_blank">&nbsp;most used business term</a> in 2020. Dedicated audio processors with ML is the necessary approach to solve some of the common video conferencing pain points.&nbsp;ML on dedicated audio processors are already addressing some common video conference pain points:<ul><li dir="ltr">Selective noise canceling. By understanding a range of scenes and action noises, certain background elements such as a fan, heater, or street noise can be removed, thereby, improving overall call quality and the ability to work in a wider variety of environments.&nbsp;</li><li dir="ltr">Hi-fi audio reconstruction. Analogous to the way budget smartphones use smart software to increase the image quality from low-quality cameras, ML in real-time audio processing can improve the audio quality from low-quality microphones.&nbsp;</li><li dir="ltr">Spatial audio for improved user experience. Already available on advanced gaming headphones and some TWS devices, advanced digital sound optimization techniques provide users a spatial audio experience. Spatial audio can create a natural listening experience for the user, which can help mitigate &ldquo;<a href="https://news.stanford.edu/2021/02/23/four-causes-zoom-fatigue-solutions/" target="_blank">Zoom Fatigue</a>&rdquo;.</li></ul>In the future, ML on audio processors will have even more advanced capabilities such as real-time speech processing that would enable automatic language translations. Additionally, ML can solve the &lsquo;on mute&rsquo; problem by becoming more aware of the user&rsquo;s context and offer prompts to unmute.<h1 dir="ltr">A look at specialized audio edge processors</h1>There are several areas audio edge processors get right to deliver exceptional audio and voice command experiences.&nbsp;<h2 dir="ltr">Noise and Distance</h2>Beamforming is the process where the device can engage its ability to focus the listening or output sound in a particular direction. Noise suppression makes conversations in everyday scenarios possible. A listening device determines the direction(s) of the voice source(s) and that of noise sources. ML classification techniques are then used to determine which beams have voice or noise in them. &nbsp;The DSP then focuses on beams with voice content only for further voice UI processing. For example, in a conferencing system, the device must identify the direction of arrival of sound, and multiple speakers must be tracked at all times in a 360-degree fashion. Noise sources can also be classified further for audio event detection like glass break, fire alarms, and gunshots, etc for further expanding the role into audio aware smart home systems.&nbsp;Proximity detection is also essential for dynamic listening and speaking experience. &nbsp;The device detects user proximity to the microphone and adjusts the microphone&rsquo;s gain. This feature enables active video conferencing for presentations, workouts, and learning environments. These features are central to the design of advanced video conferencing devices such as the new Amazon Show which features a screen that rotates to where it senses the user.&nbsp;<iframe src="https://www.youtube.com/embed/fxUSSgrHL-o?&wmode=opaque&rel=0&enablejsapi=1&origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" style="width: 789px; height: 504px;" data-gtm-yt-inspected-8="true" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe><h2 dir="ltr">Latency</h2>Humans can <a href="https://towardsdatascience.com/real-time-noise-suppression-using-deep-learning-38719819e051" rel="noopener noreferrer" target="_blank">generally tolerate</a> up to 200ms of end-to-end latency on a call before we begin to talk over each other. Low latency processing in edge processors is, therefore, a critical requirement for ensuring high-quality voice communication.<h2 dir="ltr">Power consumption</h2>Audio edge processors designed with proprietary architectures, hardware accelerators, and special instruction sets can optimally run audio and ML algorithms. These optimizations help with reducing power consumption in audio-heavy use cases such as video conferencing.<h2 dir="ltr">Integration</h2>Audio edge processors that open up their architectures and development environments accelerate innovation by providing audio application developers the tools and support to create new devices and applications. Future audio devices will be a collaborative effort.<h2 dir="ltr">Security</h2>Edge processing can minimize the need for cloud processing providing many benefits, including increased data security. For example, most consumers are not comfortable with data from personal smart home devices being transferred continuously to the cloud for processing. These fears have proven true with several <a href="https://www.theguardian.com/technology/2020/dec/23/amazon-ring-camera-hack-lawsuit-threats" target="_blank">major breaches</a> by leading device manufacturers in the last years. Peace of mind can be achieved when personal data is processed for analysis or inference at the device. An excellent example of this is a smart home security device that has been trained to hear certain event sounds, such as glass breaking, that act as a trigger to alert the homeowner. As the processing of the sound and the alert occurs on the device, it does not need to be in continuous connection to the cloud, which enhances the security of the system.<h1 dir="ltr">Delivering Audio Intelligence at the Edge</h1><a href="https://www.knowles.com/products/audio-processors" target="_blank">Knowles AISonic&trade; IA8201: Dual Core</a> is an audio edge processor specifically designed for advanced audio and machine learning applications, enabling power-efficient intelligence and privacy at the edge. It offers robust voice activation and multi-microphone audio processing with sufficient compute power to perform advanced voice processing and audio output, context awareness, and gesture control for today&rsquo;s most advanced consumer electronics. Processing voice and audio voice with an audio edge processor like IA8201 can keep the power-hungry host processor off as long as possible to extend battery life. &nbsp;In addition, it also enables new use cases for enhanced user experience by having the capability to run ML algorithms on multiple sensor inputs to deliver a more natural and contextual user experience.Audio edge processors like IA8201 have a place in a variety of audio and voice-enabled applications including smart speakers, video conferencing devices, tablets, Bluetooth headphones, and smart home devices.Recommended reading:&nbsp;<a href="https://www.wevolver.com/article/enhancing-the-utility-of-audio-ai-in-everyday-devices-using-audio-source-classification" rel="noopener noreferrer" target="_blank">Enhancing the utility of Audio AI in everyday devices using Audio Source Classification</a><h1 dir="ltr">Conclusion</h1>Dedicated audio edge processors will define the next generation of audio and voice-enabled devices creating more context-aware, immersive, and seamless audio communication experiences. Their ability to enable efficient processing for low power and low latency voice communications, noise reduction, context awareness, and accelerated ML inferencing of sensor inputs opens the possibilities to an explosion of new user experiences for the human-machine interfaces.&nbsp;<hr><h1>References and further reading:<a href="https://blog.adobe.com/en/publish/2019/04/09/contextual-ai-the-next-frontier-of-artificial-intelligence.html#gs.tuw2rt" target="_blank"></a></h1><a href="https://blog.adobe.com/en/publish/2019/04/09/contextual-ai-the-next-frontier-of-artificial-intelligence.html#gs.tuw2rt" target="_blank">https://blog.adobe.com/en/publish/2019/04/09/contextual-ai-the-next-frontier-of-artificial-intelligence.html#gs.tuw2rt</a>Antonini, Mattia &amp; Vecchio, Massimo &amp; Antonelli, Fabio &amp; Ducange, Pietro &amp; Perera, Charith. (2018). Smart Audio Sensors in the Internet of Things Edge for Anomaly Detection. IEEE Access. PP. 1-1. 10.1109/ACCESS.2018.2877523.&nbsp;Do, Ha &amp; Sheng, Weihua &amp; Liu, Meiqin &amp; Zhang, Senlin. (2016). Context-aware sound event recognition for home service robots. 739-744. 10.1109/COASE.2016.7743476.&nbsp;<a data-lf-fd-inspected-kn9eq4roawkarlvp="true" href="https://www.knowles.com/docs/default-source/default-document-library/knowles-ia8201-product-brief-final1b6a11c731dff6ddbb37cff0000940c19.pdf" target="_blank">https://www.knowles.com/docs/default-source/default-document-library/knowles-ia8201-product-brief-final1b6a11c731dff6ddbb37cff0000940c19.pdf</a><a href="https://voicebot.ai/2019/06/01/dedicated-audio-processors-at-the-edge-are-the-future-here-are-the-reasons-why/" target="_blank">https://voicebot.ai/2019/06/01/dedicated-audio-processors-at-the-edge-are-the-future-here-are-the-reasons-why/</a><a href="https://www.networkworld.com/article/3262105/how-edge-computing-makes-voice-assistants-faster-and-more-powerful.html" target="_blank">https://www.networkworld.com/article/3262105/how-edge-computing-makes-voice-assistants-faster-and-more-powerful.html</a>

Dedicated audio edge processors with machine learning will deliver increasingly natural communication experiences.

Audio edge processors deliver sublime context-aware communication experiences

The invention of Thomas Edison’s phonograph—which used a needle to etch a groove, which could then be played back by another needle following the groove’s indentations—heralded the era of recorded sound.Those grooves, first on wax with mechanical playback and then on vinyl with electrical playback, continued as the mainstay of recorded sound until after the second world war. Then the German invention of magnetic tape became the preferred media for original recordings. Tape lasted until the mid-70s when the analoge era was brushed aside by digital recording using technology pioneered by Sony.Today, the benchmark for high-quality digital sound is the CD. CD-quality digital technology samples analoge sound at 44.1 kHz with 16-bit precision (1.41 Mbps), a sampling rate sufficient to capture all the sounds humans can hear across the 20 kHz bandwidth of the ear. Such a rich stream of data currently demands a wired circuit to reproduce robustly, but wireless tech is rapidly catching up.<h2>Higher quality wireless audio with extended battery life</h2>Back in January 2020, the Bluetooth Special Interest Group (SIG) announced work had started on a specification for LE Audio. Hailed as “the next generation” of Bluetooth audio, the organization claimed the new technology—which will support wireless audio streaming over Bluetooth LE—will bring higher quality sound and extended battery life from speakers and headphones.LE Audio also brings other new functionality, including multi-stream synchronized audio for applications such as earbuds, and Audio Sharing, whereby a single audio source can be broadcast to multiple recipients.A key technical element of LE Audio, LE Isochronous Channels, was introduced as part of Bluetooth 5.2. The feature addresses a key drawback of Classic Audio by providing a mechanism that ensures multiple sink devices receiving data from the same source reproduce it simultaneously.<h2>LC3 provides another key part of the LE Audio specification</h2>A significant step towards LE Audio has just come with the Bluetooth SIG’s release of the specification detailing the Low Complexity Communications Codec (LC3). A codec is an algorithm that compresses digital data to reduce the throughput of a wireless link, while a companion algorithm decompresses the data at the other end.LC3 is an efficient Bluetooth audio codec for use in LE Audio applications. It can encode speech and music at various bitrates by supporting sampling rates of 8, 16, 24, 32, 44.1, and 48 kHz and sample resolutions of 16, 24, and 32-bits. Bit rate flexibility allows developers to trade-off sound quality against power consumption and, in turn, extend battery life or reduce battery size.LC3 is not the first codec the Bluetooth SIG has unveiled. In 2003, Classic Audio was accompanied by a low-complexity sub-band codec (SBC) designed to boost audio quality at low-to-medium bit rates. Although SBC was moderately successful, proprietary and manufacturer-defined codecs, such as Qualcomm’s aptX and aptX-HD, and Sony’s LDAC, also work well with Classic Bluetooth Audio.However, according to the Bluetooth SIG, extensive listening tests have shown that LC3 provides improvements in audio quality over SBC, with the most notable improvements occurring at low data rates. Get Connected Blog readers can check out the audible difference between LC3 and SBC by heading to an interactive listening demo on the Bluetooth website.<h2>Nordic SoC support for LE Audio</h2>With good timing, Nordic is now offering its <a href="https://www.nordicsemi.com/Products/Low-power-short-range-wireless/nRF5340?utm_campaign=2021%20wevolver" rel="noopener noreferrer" target="_blank">nRF5340 SoC</a> in <a href="https://www.nordicsemi.com/News/2020/12/nRF5340-is-now-available-as-a-production-version-through-Nordics-distribution-network?utm_campaign=2021%20wevolver" rel="noopener noreferrer" target="_blank">production volumes</a> together with a production version of the SoC’s <a href="https://www.nordicsemi.com/Software-and-tools/Development-Kits/nRF5340-DK?utm_campaign=2021%20wevolver" rel="noopener noreferrer" target="_blank">development kit</a> and <a href="https://www.nordicsemi.com/Software-and-tools/Software/nRF-Connect-SDK?utm_campaign=2021%20wevolver" rel="noopener noreferrer" target="_blank">software development tools</a>. The nRF5340 combines a high-performance application processor with a fully-programmable network processor, plus advanced root-of-trust and trusted execution security features. With an LE Audio-enabled software stack, the nRF5340’s radio supports LE Isochronous Channels, and LC3 runs efficiently on the SoC. The nRF5340 also has an on-chip audio PLL for audio synchronization for true wireless stereo playback.<hr>This article was first published on Nordic's&nbsp;<a href="https://blog.nordicsemi.com/getconnected" rel="nofollow" target="_blank"></a><a href="https://blog.nordicsemi.com/getconnected?utm_campaign=2021%20wevolver" rel="nofollow" target="_blank">Get Connected Blog</a>.

The Bluetooth SIG unveils the codec that will help power LE Audio’s enhanced sound.

One step closer to Bluetooth LE Audio

EPFL’s Laboratory for Experimental Museology (eM+), led by Sarah Kenderdine, has received support from Engagement Migros to produce muse, a pioneering audience evaluation app, in collaboration with over 20 Swiss museums. The tool provides visitors with an engaging way to communicate their experiences, while helping museums to consider this feedback when developing exhibits and other attractions. Encouraging audiences to participate in evaluations in large enough numbers to provide useful data is notoriously difficult, and for good reason: respondents may find the process time-consuming or boring. But what if you were invited to communicate your experience at a museum exhibit by making drawings, leaving voice recordings, interacting with animated graphics, or taking photos?&nbsp;This is the aim of muse: a design-led audience evaluation app that helps museums get to know their visitors better. In addition to geographic and demographic information, the tablet-based tool uses some 30 interactive elements to collect qualitative, subjective data about museum visitors’ experiences.<h2>Capturing experiences</h2>Sarah Kenderdine leads the eM+ lab in EPFL’s College of Humanities. She explains that in Switzerland, as in many parts of the world, museums usually collect only basic demographic data from their visitors, such as number of entries and origin. The goal of muse is therefore to give museums access to data analytics tools that can help them focus on many visitors as individuals, and to use the quality of visitor engagement as a method to define, measure, and program for improved museum experiences. “It is the social, political, emotional, educational, and creative elements, in addition to how deeply people are participating in a museum’s programs and exhibitions, that should be the basis for measuring value,” Kenderdine says. Unlike a traditional audience evaluation or survey, visitors use muse to record their experiences as they pass through an exhibit, rather than afterward. “Muse is always meant to be deployed inside the space of experience, and not afterward, so people can reflect in real time on what their reactions to it really are. Simultaneously, museum staff can see aggregated results on a real-time dashboard,” says Kenderdine, who has been developing muse since 2012. She notes a recent example of the success of the muse model, when a previous version of the app was deployed for the Winter at Tantora arts festival in AlUla, Saudi Arabia. It engaged 6,000 respondents across five surveys with 120 data points, resulting in 1,200 pages of reports. “This data provided a phenomenal insight into customer loyalty, future planning, and creative responses to the cultural offering at this new world heritage destination.” With the support of Engagement Migros, and the participation of more than 20 museums across Switzerland, Kenderdine’s goal now is to bring muse to the next level, including more sophisticated analysis driving better public outcomes for museums and their audiences. The project will be officially launched on September 17th, 2020 at EPFL’s ArtLab.<h2>Looking ahead</h2>At first, the muse team will partner with eight ‘core’ museums and institutions for a three-year period, including: the Château de Morges (Morges), the Haus der Elektronischen Künste (Basel), the International Red Cross Museum (Geneva), the Musée d'Éthnographie (Geneva), the Museum für Gestaltung (Zürich), the Rietberg Museum (Zürich), and the Olympic Museum (Lausanne). Each institution will be supported by an administration portal for creating questions and surveys, an app for collecting data, and a dashboard for visualizing the data in real time. A further 16 museums will be invited to join the project through an open call starting in late 2020. Throughout the four-year funding period, the muse team will develop convincing case studies for the Swiss museum landscape. It will also explore options for commercializing the app and its licensing models, and for expanding to include all cultural offerings across Europe and beyond – all in close collaboration with its partners. “With its user-centric approach, muse supports museums in their efforts to continue being active places in society,” says Johanna Muther, Project Manager at Engagement Migros. Kenderdine adds that going forward, muse will also help museums to increasingly embrace their audiences with renewed attention as they adapt to the challenges of the coronavirus pandemic. “Visitors are active participants, and not just consumers, in helping to support museums as polyvocal places. Muse gives an amplifier to those voices,” she says.<hr>About muse Muse is the voice of the visitor. In addition to quantitative data, such as geographic and demographic information, the tool can also be used to collect qualitative findings on the direct impact of an exhibition visit. The focus of the app is on creating playful interactions, and on being easy to use – for museums and visitors alike. Museums can use the findings to take greater account of the perspective of visitors when curating and developing their exhibits, thus helping them to remain firmly established as active places in society, both now and in the future.About Engagement Migros The Engagement Migros development fund enables pioneering projects in the midst of social change, which break new ground and test solutions aimed at the future. This approach is oriented towards making a difference and combines financial support with coaching services in its Pioneerlab. Engagement Migros is made possible by the companies of the Migros Group thanks to annual funding of approximately CHF 10 million; it has been complementing the Migros Culture Percentage since 2012.

The tool provides visitors with an engaging way to communicate their experiences, while helping museums to consider this feedback when developing exhibits and other attractions.

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