The Rise Of The Machines; Analogue Meets Artificial Intelligence

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When Southern Cross Austereo (SCA) become an early-stage investor in Melbourne-based Sonnant, decided to reach out to the company and ask what SCA was getting for their money.

Surprisingly, Sonnant CEO Tony Simmons responded by offering a demonstration to explain how it all worked.

Sonnant styles itself as a “transformational artificial intelligence (AI) and machine learning (ML) company that provides content discovery for the spoken word”.

Simmons explained that the key to Sonnant’s success was their initial decision to train the AI to understand the Australian accent in phase one. “There’s often problems with the machine understanding the Aussie accent. Anyone who’s tried to make a booking at a restaurant while in America will know what I mean.”

Australian English is most associated with monophthongs (single vowels), where there are approximately 20 distinct sounds compared to American English, with only 16 sounds. Also difficult for the AI are Australian diphthongs, the timing between two vowel sounds and the tendency for a falling second sound.

An accurate transcript of an analogue recording is necessary to map the keywords. Simmons shows me how the platform extracts the keywords for use in various scenarios. Furthermore, users can set parameters or search for terms that the AI hasn’t selected.

Once a user has a summary of the terms, they can attach them to various curation problems. Let’s say SCA has noticed audiences downloading their morning shows late in the day, possibly listening while commuting home. The shows need to have commercials attached that are more appropriate for that time of day; delete the breakfast cereal and add commercials for Deliveroo.

By the end of the month, Sonnant will offer users the ability to curate their listening by using keywords and subjects.

Originally Sonnant was used in the education sector. Students and staff have been able to use the program to revise and redistribute lectures. The possibilities for students to curate course notes to reflect their research or to improve weaknesses in their knowledge is continually developing.

Simmons and I discuss the possibilities; I suggest having at your fingertips the ability to retrieve archival records of Dwight Eisenhower’s wartime comments on the war in Europe, against his domestic policy priorities while he was President in the ’50s.

The Future Is Here

Simmons leans over his desk and tells me he’s calling this ability to extract useable analogue recording as ‘archival revival’.

We return to the topic of SCA and how they might apply the platform to their industry. Simmons puts it in terms of “talent and topics”.

Radio audiences will either follow individual personalities or hone in on topics. Take sports radio. Audiences will want to curate their listening to suit their interests. An AFL demographic will have different consumer biases than a basketball listening demographic. There will be cross overs, but for SCA, the ability to tailor every aspect of the listening experience will allow them to extract maximum revenue.

Finally, if you can flexibly curate your audio via AI and machine learning, you probably need less expensive human staff in the production studio. Productivity will rise, leaving more income to drop to the bottom line.

As Simmons ends, he tells me that “the technology now allows us to do things with audio that was science fiction a few years ago. There’s plenty more to come.”

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