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A team of researchers from Apple and Carnegie Mellon University’s Human-Computer Interaction Institute have presented a system for embedded AIs to learn by listening to noises in their environment without the need for up-front training data or without placing a huge burden on the user to supervise the learning process. The overarching goal is for smart devices to more easily build up contextual/situational awareness to increase their utility.
The system, which they’ve called Listen Learner, relies on acoustic activity recognition to enable a smart device, such as a microphone-equipped speaker, to interpret events taking place in its environment via a process of self-supervised learning with manual labelling done by one-shot user interactions — such as by the speaker asking a person ‘what was that sound?’, after it’s heard the noise enough time to classify in into a cluster.
A general pre-trained model can also be looped in to enable the system to make an initial guess on what an acoustic cluster might signify. So the user interaction could be less open-ended, with the system able to pose a question such as ‘was that a faucet?’ — requiring only a yes/no response from the human in the room.
Refinement questions could also be deployed to help the system figure out what the researchers dub “edge cases”, i.e. where sounds have been closely clustered yet might still signify a distinct event — say a door being closed vs a cupboard being closed. Over time, the system might be able to make an educated either/or guess and then present that to the user to confirm.
They’ve put together the below video demoing the concept in a kitchen environment.
In their paper presenting the research they point out that while smart devices are becoming more prevalent in homes and offices they tend to lack “contextual sensing capabilities” — with only “minimal understanding of what is happening around them”, which in turn limits “their potential to enable truly assistive computational experiences”.
And while acoustic activity recognition is not itself new, the researchers wanted to see if they could improve on existing deployments which either require a lot of manual user training to yield high accuracy; or use pre-trained general classifiers to work ‘out of the box’ but — since they lack data for a user’s specific environment — are prone to low accuracy.
Listen Learner is thus intended as a middle ground to increase utility (accuracy) without placing a high burden on the human to structure the data. The end-to-end system automatically generates acoustic event classifiers over time, with the team building a proof-of-concept prototype device to act like a smart speaker and pipe up to ask for human input.
“The algorithm learns an ensemble model by iteratively clustering unknown samples, and then training classifiers on the resulting cluster assignments,” they explain in the paper. “This allows for a ‘one-shot’ interaction with the user to label portions of the ensemble model when they are activated.”
Audio events are segmented using an adaptive threshold that triggers when the microphone input level is 1.5 standard deviations higher than the mean of the past minute.
“We employ hysteresis techniques (i.e., for debouncing) to further smooth our thresholding scheme,” they add, further noting that: “While many environments have persistent and characteristic background sounds (e.g., HVAC), we ignore them (along with silence) for computational efficiency. Note that incoming samples were discarded if they were too similar to ambient noise, but silence within a segmented window is not removed.”
The CNN (convolutional neural network) audio model they’re using was initially trained on the YouTube-8M dataset — augmented with a library of professional sound effects, per the paper.
“The choice of using deep neural network embeddings, which can be seen as learned low-dimensional representations of input data, is consistent with the manifold assumption (i.e., that high-dimensional data roughly lie on a low-dimensional manifold). By performing clustering and classification on this low-dimensional learned representation, our system is able to more easily discover and recognize novel sound classes,” they add.
The team used unsupervised clustering methods to infer the location of class boundaries from the low-dimensional learned representations — using a hierarchical agglomerative clustering (HAC) algorithm known as Ward’s method.
Their system evaluates “all possible groupings of data to find the best representation of classes”, given candidate clusters may overlap with one another.
“While our clustering algorithm separates data into clusters by minimizing the total within-cluster variance, we also seek to evaluate clusters based on their classifiability. Following the clustering stage, we use a unsupervised one-class support vector machine (SVM) algorithm that learns decision boundaries for novelty detection. For each candidate cluster, a one-class SVM is trained on a cluster’s data points, and its F1 score is computed with all samples in the data pool,” they add.
“Traditional clustering algorithms seek to describe input data by providing a cluster assignment, but this alone cannot be used to discriminate unseen samples. Thus, to facilitate our system’s inference capability, we construct an ensemble model using the one-class SVMs generated from the previous step. We adopt an iterative procedure for building our ensemble model by selecting the first classifier with an F1 score exceeding the threshold, 𝜃&'( and adding it to the ensemble. When a classifier is added, we run it on the data pool and mark samples that are recognized. We then restart the cluster-classify loop until either 1) all samples in the pool are marked or 2) a loop does not produce any more classifiers.”
Privacy preservation?
The paper touches on privacy concerns that arise from such a listening system — given how often the microphone would be switched on and processing environmental data, and because they note it may not always be possible to carry out all processing locally on the device.
“While our acoustic approach to activity recognition affords benefits such as improved classification accuracy and incremental learning capabilities, the capture and transmission of audio data, especially spoken content, should raise privacy concerns,” they write. “In an ideal implementation, all data would be retained on the sensing device (though significant compute would be required for local training). Alternatively, compute could occur in the cloud with user-anonymized labels of model classes stored locally.”
You can read the full paper here.
n
Five years before it finally entered the smartwatch market in earnest, Apple introduced a square touchscreen nano. Three years before the arrival of the first Pebble, people were already considering the smartwatch possibilities. Accessory makers quickly took advantage, introducing wrist bands that would let it function as a touchscreen music watch. That sixth-gen product ultimately served as a foundation for the popular device to come.u00a0n
Per Chaudhri: n
n
i had just wrapped up ios5 and took it down to show the ID team what notification centre and siri was u2013 and what it could be in the future. i never got to share it with steve. we lost him right after ios5.nn
Other interesting bits here include:n
n
- The Solar watch face was designed as u201cas a way for muslims observing ramadan to quickly see the position of the sun and for all to understand the sun’s relationship to time.”n
- The butterfly animation was created using real (albeit deceased) butterflies (one of which is now framed in his home).n
- The touch feature originally went by the name E.T. (electronic touch).n
- The Digital Touch drawing feature was inspired by his time as a graffiti artist.nn”,”protected”:false},”excerpt”:”rendered”:”
Update: We mistakenly noted in an earlier version that Chaudhri had been a part of Microsoft’s Hololens team. The story has been updated to remove the reference.u00a0 In his two decades at Apple, Imran Chaudhri worked on many of the companyu2019s most iconic product lines, including the iPhone, iPad and Mac. The designer left the […]n”,”protected”:false,”author”:699688,”featured_media”:1610079,”comment_status”:”open”,”ping_status”:”closed”,”sticky”:false,”template”:””,”format”:”standard”,”meta”:”outcome”:””,”status”:””,”crunchbase_tag”:0,”amp_status”:””,”relegenceEntities”:[],”relegenceSubjects”:[],”jetpack_publicize_message”:”Apple Watch designer reveals the device’s origins on its fifth birthday https://tcrn.ch/350vgpR by @bheater”,”categories”:[449223024,133183],”tags”:[1807,291,36265994,262,30643,580102,106595,2672,1687650,1844,637,86282,29924711,6,1180879],”crunchbase_tag”:[],”tc_stories_tax”:[],”tc_event”:[],”jetpack_featured_media_url”:”https://techcrunch.com/wp-content/uploads/2018/03/gettyimages-469156996.jpg”,”jetpack_publicize_connections”:[],”shortlink”:”https://tcrn.ch/350vgpR”,”rapidData”:”pt”:””,”pct”:””,”featured”:false,”subtitle”:””,”fundingRound”:false,”seoTitle”:””,”seoDescription”:””,”premiumContent”:false,”premiumCutoffPercent”:1,”tc_cb_mapping”:[“slug”:”apple”,”cb_name”:”Apple”,”cb_slug”:”apple-organization”,”cb_link”:”https://crunchbase.com/organization/apple”,”slug”:”apple”,”cb_name”:”Apple”,”cb_slug”:”apple-organization”,”cb_link”:”https://crunchbase.com/organization/apple”,”slug”:”microsoft”,”cb_name”:”Microsoft”,”cb_slug”:”microsoft-organization”,”cb_link”:”https://crunchbase.com/organization/microsoft”],”associatedEvent”:null,”event”:null,”authors”:[699688],”hideFeaturedImage”:false,”_links”:”self”:[“href”:”https://techcrunch.com/wp-json/wp/v2/posts/1979766″],”collection”:[“href”:”https://techcrunch.com/wp-json/wp/v2/posts”],”about”:[“href”:”https://techcrunch.com/wp-json/wp/v2/types/post”],”replies”:[“embeddable”:true,”href”:”https://techcrunch.com/wp-json/wp/v2/comments?post=1979766″],”version-history”:[“count”:9,”href”:”https://techcrunch.com/wp-json/wp/v2/posts/1979766/revisions”],”predecessor-version”:[“id”:1980005,”href”:”https://techcrunch.com/wp-json/wp/v2/posts/1979766/revisions/1980005″],”authors”:[“embeddable”:true,”href”:”https://techcrunch.com/wp-json/tc/v1/users/699688″],”https://techcrunch.com/edit”:[“href”:”https://techcrunch.com/wp-admin/post.php?post=1979766&action=edit”],”author”:[“embeddable”:true,”href”:”https://techcrunch.com/wp-json/tc/v1/users/699688″],”wp:featuredmedia”:[“embeddable”:true,”href”:”https://techcrunch.com/wp-json/wp/v2/media/1610079″],”wp:attachment”:[“href”:”https://techcrunch.com/wp-json/wp/v2/media?parent=1979766″],”wp:term”:[“taxonomy”:”category”,”embeddable”:true,”href”:”https://techcrunch.com/wp-json/wp/v2/categories?post=1979766″,”taxonomy”:”post_tag”,”embeddable”:true,”href”:”https://techcrunch.com/wp-json/wp/v2/tags?post=1979766″,”taxonomy”:”_tc_cb_tag_taxonomy”,”embeddable”:true,”href”:”https://techcrunch.com/wp-json/wp/v2/crunchbase_tag?post=1979766″,”taxonomy”:”tc_stories_tax”,”embeddable”:true,”href”:”https://techcrunch.com/wp-json/wp/v2/tc_stories_tax?post=1979766″,”taxonomy”:”tc_event”,”embeddable”:true,”href”:”https://techcrunch.com/wp-json/wp/v2/tc_event?post=1979766″],”curies”:[“name”:”wp”,”href”:”https://api.w.org/rel”,”templated”:true],”_embedded”:{“authors”:[“id”:699688,”name”:”Brian Heater”,”url”:””,”description”:””,”link”:”https://techcrunch.com/author/brian-heater/”,”slug”:”brian-heater”,”avatar_urls”:”24″:”https://secure.gravatar.com/avatar/eb77d830ad404e16ee7a4c7000b5f49d?s=24&d=identicon&r=g”,”48″:”https://secure.gravatar.com/avatar/eb77d830ad404e16ee7a4c7000b5f49d?s=48&d=identicon&r=g”,”96″:”https://secure.gravatar.com/avatar/eb77d830ad404e16ee7a4c7000b5f49d?s=96&d=identicon&r=g”,”links”:”twitter”:”https://twitter.com/bheater”,”linkedin”:”http://www.linkedin.com/pub/brian-heater/1/a92/94″,”crunchbase”:”https://www.crunchbase.com/person/brian-heater”,”position”:”Hardware Editor”,”cbDescription”:”
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The homescreen of an Apple Watch smartwatch is seen as it stands on display during a preview event at Apple Inc.’s Covent Garden store in London, U.K., on Friday, April 10, 2015. The watch, which is officially available from April 24, offers capabilities such as tracking the wearer’s health and fitness data, giving directions, sending messages and controlling music, but must be paired with an iPhone to work. 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Ventures’ Charles Hudson on ‘the conversation no one has during an upmarket’”,”content”:{“rendered”:”
For pre-seed startups, precarious times are baseline until they secure their first customer, first hire and first check. But no matter how built-in turbulence might be for a pre-seed founder, weu2019re entering a period where stresses are amplified and outlooks are unpredictable.n
In light of the new market conditions, a harder fundraising market and slower expected growth, Charles Hudson (founder and general partner of Precursor Ventures) is urging his portfolio companies to reassess their futures with a refreshingly human question: u201cAre you excited and prepared to run this company for the next two years?n
If not, you might want to do something else. Why? Because if a super early-stage company manages to survive the COVID-19 era, making it out the other end, itu2019s not clear that theyu2019ll be venture-ready when markets recover. As Hudson put it, u201cthereu2019s never been a better time to maybe fold.u201d Thatu2019s because, he explained, startups that merely survive wonu2019t be judged merely against their peers that also survived; they will also compete with brand-new startups for capital and companies that didnu2019t need to hunker down during lean times.n
Itu2019s possible to make it through, but it wonu2019t be an easy path.n
TechCrunch spoke with Hudson earlier this week as part of our ongoing Extra Crunch Live series that brings leading founders and investors to our (virtual) stage. Between our editors and journalists and the best questions from the audience, weu2019re working with guests to understand the new world that we find ourselves in. That weu2019re hosting these events virtually instead of in-person is testament to our changed reality.n
But the chat was far from all gloom; Hudson is bullish on a number of things. Niche publications with subscription economics? Yes. Social services targeting particular audiences? Yep! Precursor is still cutting checks into net-new deals, and while itu2019s wrapping up its second main fund and first opportunity fund, the firm is also raising a new, larger capital pool.n
The conversation ran the full hour we had set aside for it, meaning we had to condense some later discussions about fintech and the new trade-off between growth and profit, but we did get to diversity in venture and startups in the future, and what impact a recession might have on both (itu2019s a bigger possible impact than youu2019re considering).n
Hit the jump for the best Hudson takeaways and the full audio recording from the session. Head here if you need Extra Crunch access; there are some trials for just a few bucks, so everyone can access the chat. Letu2019s go!n
Raising a fund in the COVID-19 eran
