Find out about the problems and challenges of delivering high-quality audio for a wide range of platforms in an automated and cost-effective manner. Get the inside scoop on real use case studies that show how Emotion have worked with customers to provide solutions that integrate into their workflows.You can download a free trial of Engine here.
Webinar transcript:
Cindy:
Welcome everyone. I'm so glad you're here for our webinar today on one master, multiple deliverables. And I have MC Patel, CEO at Emotion Systems. Hey MC, how are you?
MC:
I'm good, Cindy.
Cindy:
Hey, let's dig right in and talk about one master, many deliverables. What do you mean? Tell us all about it.
MC:
So let me just go back a little bit in history. If you made a piece of content, let's say a movie, what you would have done 20 years ago is run off of a number of film prints. And then they would go on a jumbo and get distributed around to all the theaters. And similarly, if you made a episodic series, the tape would go to the network and it will be transmitted.
MC:
Now, obviously, the means of distribution have expanded dramatically. Film has demised. So now there is a great interest in saying every time you generate a piece of content, you want to monetize it as quickly as possible in as many markets as possible. And so what the industry is looking for and has to do is to create a number of masters out of that one original.
MC:
Now, there are plenty of solutions for video that allow you to do this. And for cost-effectiveness, these solutions are automated. There aren't many solutions for audio. And so what we mean by one master many deliverables is really that we get one piece of audio and we regenerate audio that's suitable for a number of platforms, a number of countries and so on. So that's one master, many deliverables.
Cindy:
Got it. And can you tell us a little bit about how Emotion Systems got into this space originally?
MC:
Sure. So we're 10 years old and we started off with an idea which was basically we wanted to make software modules. We recognized that the industry was moving away from tape and into files. And so we wanted to create software modules that would solve specific problems in the final domain. And we started off by examining the market and saying, what sort of things people are looking for?
MC:
And talking to a number of people, they said that they use their edit suites, which really are for creative purposes, to do a lot of mundane things. And we had a range of ideas, inserting bars in tone, clocks and so on. But at that time, what was very topical was loudness. The industry was changing from peak-based measurement to program loudness. And a lot of people didn't understand what loudness was.
MC:
So what happened is people came to us and said, Tell us about loudness. How do we solve that? Now, instead of trying to solve an old problem in a different way, we saw the opportunity to solve a new problem and use our technology. So we developed a product that would measure loudness in a file. But the real challenge that people had is they were very familiar with what to do with the old audio if it was wrong and so on. They weren't with this.
MC:
So they said, Well, this is great, you telling me what's wrong. What do I do about it? And so that was the germ of the idea that started Emotion off. And in essence, what we recognized is that once you've married the video and audio together in a file, it's very difficult to operate on one and do something with it. Normally you'd go into an edit suite, then you have access to the video or the audio or the metadata, and you do something with it.
MC:
So we wrote a piece of software that allowed us to read the file, take the audio out, do something with it, copy the file, copy modified audio back in, and we wouldn't have touched the video or the metadata. So Emotion started off doing loudness in this manner with a manual solution. And very quickly the market came back to us and said, This is really interesting.
MC:
But what they said is, If you can take the audio out, you can do more than loudness. I know our friends that Dolby threw a challenge to us and said, Imagine we had a fire with Dolby E, so encoded for carrying more channels. What if I needed to do loudness measurement and correction in that? So we licensed the encoders, the decoders, and came up with an algorithm, which said, If I detect Dolby E, I will decode it, I will measure the loudness or correct it. I will modify the metadata to reflect the changes I have made. I'll end code that, put that back into the file.
MC:
And the operator now doesn't know that we've just carried out a measurement and correction in a file, the Dolby E. Now, clearly they do want to know what we've done, so we generate a report. When we do the analysis, we write a report. We said, When opened the file, we found this was the loudness. These were the parameters that failed. These were the parameters that were okay, and that we corrected them.
MC:
So that was the process some six years ago or so. And then a company that had a slightly inverse problem to what we're trying to solve, but I'm just telling you the story now. The company said, Well, we get from our clients video files with 14 variations of audio. And I'll tell you about that in a second. But what they said is, We want to Because we need to loudness correct. We need to Dolby encode. And some of the tracks are not in the right place so we need to map the tracks. And there are 14 different workflows.
MC:
So for example, their normal expectation would be stereo and 5.1. And what they would do is they would loudness correct both of those, Dolby encode the 5.1. And then the output would be stereo Dolby E. And the stereo would
