TVU Networks’ AI Tech Helps Broadcasters Deliver Customized Content

Already incorporated into several TVU products, AI is at the center of TVU’s story-centric workflow, which automates content distribution and allows broadcasters to customize – and therefore better monetize – live and archived media assets.

Customers across the world already rely on TVU’s mature AI solutions to accelerate news production and distribution. CNN Newsource, for example, provides an IP-based worldwide network of affiliates with access to breaking news, trending stories, and archival video content. When ingested through the TVU MediaMind Appliance, its live feeds are enhanced with real-time metadata generated by AI-based voice and facial recognition technologies.

As a result, CNN affiliates can find the live content they need in seconds by searching person of interest, geographic location, or other details in the metadata. With easier access to more appropriate content, broadcasters enjoy improved productivity and significant time savings.

Today’s consumers have a wide variety of platforms for consuming content and can choose to see a story on a smartphone, website, or TV. However, broadcast production is historically programmatic rather than story-centric. In the story-centric workflow, all media assets are shared, with content collected in a central location that allows any production group within a station to produce it the way they want for their viewers. This mass content customization for an audience of one is driven by Media 4.0.

TVU MediaMind
With TVU MediaMind, live feeds are enhanced with real-time metadata generated by AI-based voice and facial recognition technologies.

TVU MediaMind can automate the content distribution process for specific platforms, from traditional broadcast to social media sites, for even more efficiency. The TVU CAS (Contribution Automation Solution), which streamlines the capture and automatic metadata tagging of live video content, integrates with major MAM platforms including AP ENPS, Dalet, and Primestream.

A context-based AI solution is also used effectively in TVU Transcriber, a real-time, speech-to-text transcription service. TVU Transcriber can embed text into a video stream for closed or open captioning, as well as output a file for auditing purposes. While the caption information is important for compliance, the data provides very accurate and detailed metadata, which also improves the ability to search assets.

The TVU MediaMind user interface, which keep benefitting from regular upgrades, offers improved facial recognition accuracy and management, as well as a new Microsoft celebrity face database of more than one million celebrities. Users can also add or delete faces, plus search the database using a photo. TVU MediaMind also offers three times faster cloud interface loading speed for video preview, increased recording time up to 24 hours, and auto-prompt searching keywords.

“Machine learning and AI are more than just buzzwords, and TVU is harnessing the power of these technologies to deliver ‘smart studio’ solutions and bring about the evolution of Media 4.0,” said Paul Shen, CEO of TVU. “Our industry-leading AI is improving the efficiency at every stage of the media supply chain, from transcription services to real-time live feed searches, which benefits the story-centric workflows of everyone from small-market stations to major international networks. At IBC, we look forward to showing attendees how our established AI and machine learning technologies are helping to accelerate news production and reduce costs.”

More info

How mature is AI development throughout the broadcasting industry?

By Chem Assayag. Chem Assayag is VO’s Executive Vice President of Sales and Marketing. With a strong experience in the world of digital television and content services, and during his tenure at OpenTV, the worldwide leader in interactive television, he managed operations in Europe and the Middle East, growing revenues in the company’s largest business region.


IBC recently ran a feature looking at AI in the broadcast industry and came across real world projects as diverse as using AI to automatically generate metadata, generate intermediate frames and thus super slo-mo from regular camera feeds; analyze audience behaviour patterns, provide speech to text, refine complex workflows, and much more.

Much of it is to do with automation in the value chain and currently it is mostly this that is driving the broadcast industry uptake. Broadcast is a very process-oriented business and many of these processes — think of Quality Control, monitoring channels for rights purposes, conforming, closed caption generation, ingest… the list is a long one —  map well into automation. The use of technology to increase speed and efficiency while at the same time cutting costs, is an attractive proposition.

Where is AI in Broadcast in 2019?

First off, it has to be said that, at time of writing, there is a lot of hyperbole regarding AI technologies and their impact in the value chain. A lot of what is being talked about is either projection, prototypes, or concerns projects that have a so far rather limited scope. The hype machine is in full flow as companies looking to leverage AI technologies look to raise funding and maximize interest.

This is not helped by a media that is fully buying into the more fanciful elements of the AI story. Sometimes these showcase legitimate concerns as well as rattling the funding tip jar, such as the story headlined ‘New AI fake text generator may be too dangerous to release, say creators’, which highlighted the problems of text-based deep fakes. On the whole though, there is a depressing willingness to buy into the trope of the inanimate becoming animate and leading to disaster, which is a cultural artefact you can trace from Talmudic stories of golems, through Frankenstein, and on to the Terminator movies and SkyNet; a point which the same newspaper as the previous link adroitly made a few months earlier in a piece titled ‘The discourse is unhinged’: how the media gets AI alarmingly wrong’.

Indeed, in its introduction to a gated reportexamining the hype cycle in relation to AI, analyst Gartner wrote: “AI is almost a definition of hype. Yet, it is still early: New ideas will surface and some current ideas will not live up to expectations.” Away from there alarmist mainstream headlines the AI-based technologies it sees as currently sliding into the Trough of Disillusionment (see here for an explanation of the Hype Cycle phases) include such high-profile cases as Computer Vision, Autonomous Vehicles, Commercial Drones, and Augmented Reality.

So, where are we in broadcast? Gartner has also produced a useful AI Maturity Model (see below) where companies and industries can measure their progress along a line that illustrates the growing deployment and impact of the technology.

ai in broadcasting diagram

As a whole, at this stage early in 2019 the broadcast industry is probably strung out in a line somewhere between the start of Level 2 and the early stages of Level 3. Level 2 is Active, and defined as AI appearing in proofs of concept and pilot projects, with meetings about AI focusing on knowledge sharing and the beginnings of standardization.

The Level 3 Operational stage sees at least one AI project having moved to production and best practices, while experts and technology are increasingly accessible to the enterprise. AI also has an executive sponsor within the organisation and a dedicated budget.

Things are moving swiftly though. IBC2017 was, after all, only eighteen months ago. But one of the accelerants for the introduction of technology is that the broadcast industry has been moving into the cloud at the same time. Companies no longer need to invest in their own infrastructure, hardware and software to implement AI in the value chain; they can outsource it via the cloud.

This is becoming easier to do than ever as well. As an illustration of what is available, AWS has a Free Tier that it bills as offering ‘Free offers and services you need to build, deploy, and run machine learning applications in the cloud’. The cloud-based machine learning services organizations and individuals can hook up to for no cost include:

  • Text to speech: 5 million characters per month
  • Speech to text: 60 minutes per month
  • Image recognition: Analyze 5000 images and store 1000 face metadata per month
  • Natural language processing: 5 million characters per month

Even the full costed versions can make a compelling argument. The Amazon Transcribe API is billed monthly at a rate of $0.0004 per second, for instance, meaning a transcript of a 60-minute show would cost $1.44. And, of course, though it is enormous and has a global reach, AWS is only one of a growing number of cloud-based companies offering AIaaS.

AI is Augmentation over Automation

One key point to make about AI in 2019 is that the industry is still largely working out the use cases. Automation is only the start of it, and indeed only looking at the places in a broadcast workflow where AI can automate processes is to underestimate the potential of the technology. AI holds out the promise of augmenting human actions, of being able to analyze information and make predictions based on those results faster than humans can.

That means when examining loci in a workflow where the technology can help, there are a few key considerations:

  • Does expert knowledge add value?
  • Is there a large amount of data to be processed?
  • Is the organization looking to affect an outcome included in that data?

If the answer to all those three questions is yes, then that is a business point that can be further augmented by AI.

AI in Broadcasting: All About Context

As we’ve said, there are lots of use cases and projects involving AI currently underway across the industry, but we’ll end by highlighting one example of what can be achieved in the field, the UK’s Channel 4 and its trials of Contextual Moments. This is an AI-driven technology that uses image recognition and natural language processing to analyze scenes in pre-recorded content, producing a list of positive moments that can then be matched to a brand.

Low scoring moments are discarded, whilst all candidate moments are checked by humans to ensure brand safety. After that, to use C4’s example, a baking brand might previously have contextually advertised around a show such as ‘The Great British Bake-Off’, now it can be presented with a list of programs where baking happens in a positive light, from dramas to reality shows.

Channel 4’s initial testing with 2000 people showed that this AI-driven contextual version of targeted advertising, boosted brand awareness and doubled ad recall to 64%. It will be interesting to see how those results are mirrored in real world data.

As yet, the Level 3 ideal of AI having a dedicated budget within an organization is largely fanciful; we are at too early a stage for ROI data to be reliably collated. The tendency for it to be applied mainly for automation purposes limits its impact, even though cost reductions can be impressive. More complex projects and thus more strategic impacts are on the way, though.

Gartner’s Level 4 of AI implementation sees all new digital projects at least consider AI; new products and services have embedded AI, and AI-powered applications interacting productively (and, presumably, with a degree of autonomy) within the broadcast organization. Given the speed of the timescales so far, you wouldn’t want to bet against some of the companies at the forefront of AI development starting to push into that territory towards the end of the year.

Read the full article on Viaccess-Orca website

viaccess-orca-banner

ENCO to Highlight enTranslate Automated Live Translation System with Machine Learning at IBC2019

Two dozen languages are officially spoken in the European Union, making the upcoming IBC exhibition in Amsterdam an ideal forum for the first showing outside North America of ENCO’s new enTranslate automated live translation system. Making broadcast and AV content accessible to non-native speakers for a fraction of the cost of traditional translation services, the award-winning solution will make its European debut in stand 8.A59 at the event from September 13 to 17.

ENCO_enTranslate

enTranslate offers broadcasters, media producers and live presenters an easy and affordable solution to automatically translate television programming, corporate meetings, government sessions, lectures, sermons, training materials and other content. TV broadcasters can offer subtitles and secondary or tertiary closed captions in alternative languages to expand their audiences, while government institutions, universities, houses of worship and corporations can embed translated captions in short and long-form VOD content or show live, open-captioned subtitles on local displays to assist in-person attendees.

enTranslate combines the highly-accurate, low-latency speech-to-text engine from ENCO’s patented enCaption open and closed captioning solution with advanced translation technology powered by Veritone, enabling automated, near-real-time translation of live or pre-recorded content for multi-language captioning, subtitling and more. Blending artificial intelligence with sophisticated linguistics modelling, enTranslate uses a Neural Machine Translation methodology to provide high-quality translations based on the context surrounding the current words and phrases. enTranslate supports 46 languages including English, French, Spanish and more.

“By making video content understandable to viewers who don’t speak its original language, enTranslate continues our long-standing mission of helping broadcasters and producers make their media accessible to a wider range of people,” said Ken Frommert, President of ENCO. “Traditional translation services can be prohibitively expensive and – particularly for live content –inconvenient, requiring advance scheduling of human translators. enTranslate translating live and pre-recorded content both practical and affordable for organizations large or small, and we are looking forward to showcasing it to IBC attendees.”

Deployable on-premises or in the cloud, enTranslate’s flexible architecture supports a wide range of live baseband and network-based audio and video inputs including analog, AES, HDMI, SDI, AoIP, NDI® and MADI. Translated results can be output in standard caption file formats; embedded as closed captions using an external or optional integrated encoder; or keyed as open captions over an SDI, HDMI or NDI® output. For offline, file-based applications, audio or video clips can be easily ingested into the system and captioned with translations in any supported language, enabling users to quickly and affordably process large libraries of previously recorded content.

More info

Veritone & Golden Boy Present at AWS M&E Symposium

Veritone, Inc. announced that Solution Engineer Garron Bateman will give a joint presentation at the AWS M&E Symposium. Bateman will join fellow speakers including Golden Boy, a Veritone Digital Media Hub client and one of boxing’s most active and respected promoters, for the session titled “Using AI/ML to Monetize Video Archives.”

Veritone_Golden-Boy-Digital-Media-Hub

The coffee-talk-style session will highlight how Veritone Digital Media Hub helps M&E organizations and content creators manage, distribute, and monetize their content and how aiWARE’s cognitive services can be leveraged to index and enrich media files for quick search and discovery. Bateman, together with Golden Boy’s Jackie Grant, manager of international television, and David Tetreault, executive vice president of media and entertainment, will describe the Digital Media Hub use case for Golden Boy, including their plans to leverage AI and machine learning (ML) within their branded portal, their move from an on-premise to a cloud-based media asset management solution, and more.

“Veritone’s powerful and easy-to-use digital asset management solution has increased our efficiency and improved the organization and accessibility of our content,” said Grant. “During our presentation with Garron, we will highlight the benefits we have seen since moving to a Digital Media Hub and the expected advantages of AI and ML in monetizing our content.”

More info