UnderВ Encoding settingsВ ->В Video, setВ Resolution (w x h)В to 320Г—240 andВ Bitrate (bits/s)В to вЂњ200kвЂќ.

Please note that we have to output a video in the MediaConvert job, so we will configure low resolution and bitrate video settings to reduce processing time and cost.

InВ Output groups,В click onВ Add and selectВ File group.

You can adjust the End timecode value for your needs, or skip the Input clips configuration in order to analyze the loudness of the complete source file. This timecode corresponds to 1 minute, which means only the first 60 seconds of the source file will be processed by the template.

Click on Add input clip and enter вЂњ00:01:00:00вЂќ for End timecode.

In Audio selector 1В ->В Selector type, select вЂњTrackвЂќ and enter вЂњ1вЂќ.

Under Video selectorВ ->В Timecode source, select вЂњstart at 0вЂќ.

In InputsВ section, click on AddВ button to add new inputs:.

This name will be referenced in LambdaMediaConvert (Step 6).

In the MediaConvert console, chose Job templatesВ from the menu on the left and click Create template.

Note: All AWS services in this workflow must run in the same region. To create the MediaConvert template, log into the AWS Management Console, go to the MediaConvert service, and select the region you would like to work in. The job template will be used by the function we will create called вЂњLambdaMediaConvertвЂќ to build loudness measurement job settings. Step 1: Create a MediaConvert job template

LambdaLoudnessResults collects Loudness logs from S3 to determine silent audio tracks by comparing Loudness values to a specific threshold, then updates DynamoDB with the results.ДЄll steps required to build this workflow on AWS are explained in the post.

Using an Events rule, CloudWatch filters on вЂњMediaConvert EventвЂќ and triggers the second Lambda function (LambdaLoudnessResults).

As soon as the job completes, MediaConvert sends a вЂњMediaConvert EventвЂќ to Amazon CloudWatch.

MediaConvert measures the Loudness of each audio track and saves Loudness logs into an S3 output bucket.

LambdaMediaConvert first runs MediaInfo on the source in order to determine the number of audio tracks, pushes a new job to MediaConvert, and stores job data in Amazon DynamoDB.

S3 triggers the first AWS Lambda function (LambdaMediaConvert).

A new source asset is uploaded to an Amazon S3 bucket.

The workflow diagram for this solution is shown below: If the Input Max Loudness value of an audio track is lower than -50 dB, that track is considered silent audio. The produced loudness logs provide the Input Max Loudness value for each audio track that we will compare to a threshold of -50 dB. The workflow is flexible, so we can analyze more than 60s if required. We will be using MediaConvert to analyze loudness levels of the first 60s of media source assets. It also allows logging loudness levels and storing these logs in S3. MediaConvert configuration allows the selected algorithm to only produce loudness measurements. The Audio Normalization feature of MediaConvert makes it easy to correct and measure audio loudness levels, supporting the ITU-R BS.1770-1, -2, -3, and -4 standard algorithms. The workflow is automated by AWS Lambda functions and triggered by file uploads to Amazon S3. In this post, we will create a workflow using AWS Elemental MediaConvertВ to analyze audio tracks of media assets and measure their loudness.

On the other hand, if six tracks have audio and two are silent, the asset should be sent to a 5.1 workflow for processing. From the example above, if six audio tracks are identified as silent, the asset would be processed as a stereo 2.0 asset. To build an automated video processing workflow that makes intelligent decisions based on audio, we need to detect silent audio tracks and their position in the source mezzanine assets. For example, MXF media assets may be produced with eight mono audio tracks for content that contains only 2.0 audio, so only two tracks are actually used while the remaining tracks are filled with silent audio. When sharing video-on-demand (VOD) content, produced mezzanine files often have same number of audio tracks regardless of how many are in the actual content. In file-based video processing workflows, content providers often standardize on the mezzanine file format used to share content with their customers and partners.

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