In today's fast-paced media environments, more new content is being created than production teams can possibly manage without specialized tools. It's critical that all media assets be logged and tagged so that they can be found easily, but teams have no time to do this essential work. In addition, the current generation of media asset management tools has evolved in an environment where they have been starved of metadata. As a result, content teams' options are limited to pulling technical metadata from media files or streams, extracting meaning from file and folder names, or manual logging.
Artificial intelligence is beginning to change how media organizations meet these challenges. A new and emerging breed of AI platforms for media analysis, when paired with leading-edge media asset management tools, offers great potential for transforming media workflows and making it easier than ever for operations to access, manage, and archive huge volumes of content. Through powerful tools such as speech-to-text and automatic language translation, AI engines bring new power to the MAM task of logging and tagging content—with the ability to tag assets automatically based on attributes such as people, places, things, and even sentiment.
The AI-MAMS is a series of web based app subsystems that smartly automate all the radio and television chain including pre and post production, archiving and broadcasting. The system flexibility, scalability and modularity are guaranteed by the most integrated and processed oriented solutions and cutting edge app's technology.
AI-MAMS is an AI-enabled MAM system with object, text and audio recognition capabilities that automates media tagging for avoiding time consuming manual tagging and metadata extraction processes.
Premium features of AI-MAMS are as follows:
- Fully web based environment
- Dynamic workflow management
- Automatic metadata extraction
- Report generator tools
- Comprehensive Audio and Video format support
- DAS, NAS and SAN-Based Solutions
The AI-MAMS includes following modules:
Content Watch Module:
This module continuously monitors new files and creates their low quality versions (LowRes) for AI-MAMS.
Search and Display Module:
In an effort to make the collection of resources be simply available to the users, AI-MAMS suggests a top search engine for accurate and fast resources retrieval. It also presents various features such as tree search, simple search and advanced search with considering amount of difference details.
Support of various file formats is a key feature of AI-MAMS. Visual display module is in accordance with HTML5 which represent fast loading and different web based formats display.
Workflow management is for content display, performance improvement and monitoring of defined sequence of task arranged as workflow's application using standardized structures. It includes definition of process based on diverse positions of contents, organizational notes, and accessibility to metadata levels according to user activity.
AI-MAMS workflow management suggests definition of various workflow management, definition of conditional state, allocation of contents states and allocation of users access level, creation of interstate links, automated unique code production for each state and many other features.
Play List Generator Module:
Fast and easy broadcast scheduling is an essential requirement in Radio & TV programs. With combining various capabilities, AI-MAMS deploys fast search, accessing to desired contents, displaying of play lists and setting up different schedules.
Generating tailored reports to different organizational requirements at user and managerial levels in form of various charts and tables create a useful facility for supervision, assessment, performance control and comprehensive program editing. This module can exports reports in the forms of CSV or XLS formats.
Hierarchical storage management (HSM) is a data storage technique that automatically moves data between high-cost and low-cost storage media. HSM module in AI-MAMS is a management system to support file management of various online, near-line and offline storage devices.
AI-MAMS offers the best network roots for file transmission and optimum use of network bandwidth and support HTTP, FTP, RESTful API protocols as well as backup and restore of file on LTO with standardized LTFS format.
AI-MAMS is leveraging AI capabilities that will transform media workflows and make it easier than ever for operations to access, manage, and archive tremendous volumes of content. AI-MAMS will showcase AI logging, tagging, and search capabilities based on the world's leading image and video analysis platforms. Through these integrations, AI-MAMS will offer a range of advanced capabilities including:
- automatically extract sport elements from Videos
- Pornography and nudity recognition
- Place analysis, including identification of buildings and locations
- Object and scene detection (e.g., daytime shots or shots of specific animals)
- Logo detection, to identify when certain brands appear in shots
- Text recognition, to enable text to be extracted from characters in video
- People recognition, for identifying people, including executives and celebrities
Design methodology and implantation:
- development environment: Agile development with XP and SCRUM
- Development framework: CodeIgniter
- Software architectural pattern: HMVC
- Client side development framework: AngularJS and JQuery
- Architectural structure: Service Oriented Architecture (SOA)
- Database: Open Source MySQL RDBMS
- Case-tools: Oracle DbSchema
- Video library: FFmpeg