Cognitive Services

Detecting “Action” and “Cut” in Archival Footage Using a Multi-model Computer Vision and Audio Approach with Azure Cognitive Services

September 27, 2021 Sep 27, 2021 09/27/21

Nile Wilson

Movies and TV shows require multiple takes per scene and may have a substantial amount of archival footage as a result. Here, we use Azure Cognitive Services and custom code to develop a multi-model Machine Learning (ML) solution to automatically detect discardable footage to save media companies manual archiving hours and storage space.

Building an Action Detection Scoring Pipeline for Digital Dailies

September 20, 2021 Sep 20, 2021 09/20/21

Samuel Mendenhall

Media companies capture footage filmed for the entire day in what's known as ‘digital dailies’. When talking about terabytes and petabytes of content, storage costs can be a factor. Lets explore Machine Learning approaches to identify which content can be archived or discarded which will save on those storage costs.

Entity Disambiguation Using Search Engine

June 14, 2021 Jun 14, 2021 06/14/21

Maysam Mokarian

This blog post proposes a methodology to disambiguate misspelled entities by comparing the search retrieval performance with different custom search analyzers in a search engine.

Using Azure Cognitive Services to Analyse Evidence in Public Safety and Justice

June 7, 2021 Jun 7, 2021 06/7/21

Sonal Patil

A scenario commonly encountered in public safety and justice is the need to collect and index digital data recovered from devices, so that investigating officers can perform evidence-based analysis. We recently built an advanced evidence analysis platform that uses Azure AI services for automated labelling of media and documents.

Social Stream Pipeline on Databricks with auto-scaling and CI/CD using Travis

December 12, 2018 Dec 12, 2018 12/12/18

Mor Shemesh

This code story describes CSE's work with ZenCity to create a data pipeline on Azure Databricks supported by a CI/CD pipeline on TravisCI. The aim of the collaboration was to create a pipeline capable of processing a stream of social posts, analyzing them, and identifying trends.

Semantic Segmentation of Small Data using Keras on an Azure Deep Learning Virtual Machine

July 18, 2018 Jul 18, 2018 07/18/18

Margaret Meehan

Golf performance tracking startup Arccos joined forces with Commercial Software Engineering (CSE) developers in hopes of unveiling new improvements to their "virtual caddie" this summer.

Making sense of Handwritten Sections in Scanned Documents using the Azure ML Package for Computer Vision and Azure Cognitive Services

May 7, 2018 May 7, 2018 05/7/18

Tingting Zhao

Extracting general concepts, rather than specific phrases, from documents and contracts is challenging. It's even more complicated when applied to scanned documents containing handwritten annotations. We describe using object detection and OCR with Azure ML Package for Computer Vision and Cognitive Services API.

Giving meaningful context to social media influence with Microsoft Cognitive Services

December 7, 2017 Dec 7, 2017 12/7/17

Rachel Weil

When it comes to identifying and understanding influential voices in social media, context is key. See how we used topic detection, sentiment analysis, and Azure Functions to automate context-aware social media insights.

Comparing Image-Classification Systems: Custom Vision Service vs. Inception

December 5, 2017 Dec 5, 2017 12/5/17

Clemens Wolff

This story covers how to get started with transfer-learning and build image classification models in Python with the Custom Vision Service. We compare the results with the popular Tensorflow-based models Inception and MobileNet.

Building a Custom Spark Connector for Near Real-Time Speech-to-Text Transcription

November 1, 2017 Nov 1, 2017 11/1/17

Clemens Wolff

This post describes in detail the Azure Cognitive Services speech-to-text WebSocket protocol and shows how to implement the protocol in Java. This enables us to transcribe audio to text in near real-time. We then show how to feed the transcribed radio into a pipeline based on Spark Streaming for further analysis, augmentation, and aggregation. The Java client is reusable across a wide range of text-to-speech scenarios that require time-efficient speech-to-text transcription in more than 10 languages including English, French, Spanish, German and Chinese.

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