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content recognition software
Transcript of content recognition software
Until recently, companies had to rely on a human being to detect copyright violations and then take action.
Social networking sites like facebook, orkut & video provider sites like YouTube normally count on users to report inappropriate material, but some don't consider clips that violate copyright law to be inappropriate.
At the moment, most companies have to rely on employees to uncover proprietary video footage and log a report.
It's a tedious, inefficient process that may soon become unnecessary.. Thanks to content-recognition software. Software Characteristics.. Such softwares analyze audio and video clips, compare them to a database of content & determine whether they are from sources that are protected by copyright.
Such software provides an efficient and relatively inexpensive alternative to combing through the vast amount of content on the Internet.
To add on it's far more reliable. Challanges at the door... There are dozens of ways to encode a sound or video file, so creating a program that looks for matching code isn't very useful. Viz. a WAV file and an MP3 file of the same song won't look the same from a programming-language perspective.
Songs and videos can be recorded at different bit rates, which means that two MP3 files of the same song may not match.
Software that identifies songs via cell phone has to be able to identify the track despite the quality of the recording or the interfering background noise.
Some video pirates bring recording devices into films and capture movies on their own cameras.
Some projectionists set up a digital video camera in the projection room, recording a first-run movie on its premiere night.
Videos might be cropped or altered.
Considering the scenario, any program designed to find recordings like these can't rely only on programming language or identical files. Audio-Content Recognition Software Video-Content Recognition Software A record company assembles a database of material that other files can be compared against, i.e. the company's entire music catalog.
The content-recognition software analyzes each song and creates a digital tag identifying that song.
Tags are called fingerprints or signatures.
The software analyzes the actual sound of the song rather than its encoding language.
Some programs analyze the tempo and beat of a song. Others measure the song's amplitude and frequency.
Fingerprinting software usually takes several samples that last just a few seconds each from a single recording.
A few companies offer software that analyzes entire audio clips in order to get as complete a fingerprint as possible.
One current product analyzes a song for landmarks -- distinctive acoustic moments in the clip -- then analyzes the sound around the landmarks. Ideally, the landmarks will be readily identifiable when scanning other music.
The programs use algorithms to analyze sound. Most are a type of Fast Fourier Transform (FFT) algorithm.
This mathematical technique can take a complex series of signals and track any changes within it.
These changes -- whether they're tempo changes, beats per minute or the amplitude and frequency of the sound in the clip -- are mapped out and mathematically converted into a digital fingerprint.
Fingerprints are usually in numeric form.
In order to ensure that content-recognition software identifies songs no matter what format they're in, programmers concentrate on only analyzing sounds that are within the human range of hearing, just like MP3 files.
Once a record company establishes its database, it's ready to help identify songs to potential customers or to track down cases of copyright infringement. Sound identification Often, sound clips being analyzed are not clean copies of a song.
The song could be truncated, or it might be similar to a different song.
The algorithm's job is to compare the fingerprints and determine if the incoming sound clip matches a song (or portion of a song) in the database within a certain range of probability.
There is no standard probability range for content-recognition software.
Most programs allow customers to adjust the level of similarity required to declare a match. Eg. you could adjust the program so that it only brings back match results if the algorithm determines that there is a 95 percent or better chance it's a match. If the incoming clip doesn't fall in that range, it sends an error message to the user.
When the program determines a match, a partnered application can take over. The application might send information to someone who wants to know the title of a song, or it might flag a song on a Web site and e-mail the corresponding record company's legal department.
Some record companies have used such software to scan file-sharing sites or to track content on Web sites that stream audio. The entire process of analysis and matching takes only a few seconds. Video content-recognition software is similar to existing audio content-recognition programs in that it analyzes content to create a fingerprint.
Then it compares that information to fingerprints in a database to determine if there is a match. The unique challanges imposed... Most videos on YouTube are limited to 10 minutes or 100 megabytes. Since a clip could include any 10-minute segment from a film or television show under copyright, the content-recognition software must analyze the entire original work in such a way that it can make meaningful matches from a relatively small sample clip.
The program analyzes overlapping chunks of the original content to create multiple fingerprints.
Video content-recognition software must be able to identify footage even if the person who uploaded the content edited it first.
Viz. people can fool software that matches color resolution by tweaking the color saturation in a video.
Cropping a video or uploading footage of a film captured on a video camera can also hinder recognition software.
Some pirated films are captured on cameras set up at an angle to the screen, further complicating the identification process.
One approach uses programs to base fingerprints off an analysis of the changes in motion characteristics in a video.
Even this could prove ineffective if someone uploads a pirated video captured on a hand-held camera.
The sheer volume of video content presents a big problem.
Movie and television studios will need to constantly update their databases with fingerprints for all the new content that comes out every day.
While the process for uncovering piracy may become more efficient, it will still require constant upkeep and maintenance. ....The Universal Barcode for Music
Relatable® is a leading provider of advanced content identification and personalization technologies for the digital delivery of audio and video content.
Its advanced acoustic fingerprinting technology is a leading solution for identifying digital music and media files.
It recognizes songs and audio content based on the acoustical properties in the audio itself, and has been developed to achieve maximum accuracy in discriminating between different songs, as well as identifying each and every digitized copy of a recorded song, regardless of audio file format, bit rate or common signal distortions.
As an add on, it delivers unprecedented speed and can scale to meet the needs of any size network. Relatable Tuneprint Tuneprint is a recognition system that allows software to "listen to" and identify audio.
It is effective without regard to format or compression, and requires no watermarking or prior exposure to any specific file.
Existing media systems majorly rely on reading the notoriously unreliable "hints" available in filenames and ID3 tags to provide users with basic features, like displaying a song title or searching for a specific artist.
By listening to the content of the media file, Tuneprint offers these systems reliable metadata, eliminating the limitation to whatever information happens to be embedded in the file.
Tuneprint uses innovative psychoacoustic and statistical technologies to create a unique "fingerprint" for each audio recording.
A five-second clip of any audio can then be matched against a database of these fingerprints, quickly and reliably obtaining the identification and associated metadata of the recording.
Audible Magic On 25 feb, 2007 Vance Ikezoye, the chief executive of Audible Magic in Los Gatos, California, demonstrated the technology by downloading a two-minute clip from YouTube and feeding it into his company's new video-recognition system.
The clip — drained of color, with dialogue dubbed in Chinese — appeared to have been recorded with a camcorder in a dark movie theater before it was uploaded to the Web, so the image quality was poor.
Still, Ikezoye's filtering software quickly identified it as the sword- training scene that begins 49 minutes and 37 seconds into the Miramax film "Kill Bill: Vol. 2."
This technological weapon makes it possible to identify copyrighted material, even from blurry video clips. DSP boards Features..
* High-Performance -- uses DSP Farm board to offload processing load from the PC to allow simultaneous multichannel processing
* Content based algorithm - independent of filename, labeling, transmission format. Resistant to noise, watermarking, repeated MP3 or other compression, etc.
* Real-time operation supported up to 8 channels per DSP board with active database limited to 512 items
* Detection of content impairments including truncation, time compression/expansion (up to 3%), editing/excerpting, and others
* Supports line-level analog inputs, balanced or single-ended, with software programmable input gain, output attenuation, and ac/dc coupling Attrasoft The Attrasoft solution is an automated content identification platform based on internally developed image and pattern recognition algorithms.
Attrasoft converts digital video content to fingerprints and makes Fast, Accurate, & Scalable Video Content Recognition via the fingerprints.
Attrasoft KeepWatch is a stand-alone system, which consists of both software and hardware for content recognition. References computer.howstuffworks.com/content-recognition.htm/
www.attrasoft.com The software matches fingerprints that represent a sound's waves to try to get a match. ACR System GUI, with waveform diagnostic display shown Audio Content Recognition System GUI, with statistics view screen shown Video pattern matching Summary A technical approach with clearly defined goals is what is needed to combat the current piracy invasion.
The various software developed by different organizations are opening new frontiers to enable right protection of various individuals & business firms.
Such steps ensure not only social justice but facilitate the deservers earn their right share recognition.
Employing a combination of these technologies can help firms protect their valuable assets.
So a more judicious approach would command that first the goal be defined what needs to be protected from whom, then measures be taken to choose the right technology which most appropriately addresses the problem in hand. Guided by : Ms. Aditi Bhutani Submited by : Kanupriya V. Tulsyan
Exam No : 43