Second, I’m guessing, your account will be banned. Third, you cry and wonder why you ever violated copyright in the first place.

In my case, I’ve disputed every one of the 4 copyright violation notices that I’ve received under grounds of Fair Use and Fair Dealing. Here’s what happens when you file a dispute using YouTube’s online form (click for high-res):

3 of the 4 have been dropped after I’ve filed disputes, though I’m still waiting to hear about the response to the above dispute. Read the dispute letter to Sony ATV and UPMG Publishers in full here.

The picture above shows a few stills from what my Smash Ups look like. The process described in greater detail on createdigitalmotion.com is part of my ongoing research into how existing content can be transformed into artistic styles reminiscent of analytic cubist, figurative, and futurist paintings. The process to create the videos … Continue reading...

The post YouTube’s “Copyright School” Smash Up first appeared on http://archive.pkmital.com.

Dear Sony ATV Publishing, UMPG Publishing, and other concerned parties,

I ask you to please withdraw your copyright violation notice on my video, “PSY – GANGNAM STYLE (?????) M/V (YouTube SmashUp)” as I believe my use of any copyrighted material is protected under Fair Use or Fair Dealing. This video was created by an automated process as part of an art project developed during my PhD at Goldsmiths, University of London: http://archive.pkmital.com/projects/visual-smash-up/ and http://archive.pkmital.com/projects/youtube-smash-up/

The process which creates the audio and video is entirely automated meaning the accused video is created by an algorithm. This algorithm begins by first creating a large database of tiny fragments of audio and video (less than 1 second of audio per fragment) using 9 videos from YouTube’s top 10 list. From this database, the tiny fragments of video and audio are stored as unrelated pieces of information and described only by a short series of 10-15 numbers. These numbers represent low-level features describing the texture and shape of the fragment of audio or video. These tiny fragments are then matched to the tiny fragments of audio and video detected within the target for resynthesis, in this case the number one YouTube video … Continue reading...

The post An open letter to Sony ATV and UMPG first appeared on http://archive.pkmital.com.

I’ve been interested in exploring ways of navigating media archives. Typically, you may use iTunes and go from artist to artist, or have managed to tediously classify your collection into genres. Some may still even browse their music through a file browser, perhaps making sure the folders and filenames of their collection are descriptive of the artist, album, year, etc… Though what about how the content actually sounds?

Wouldn’t it be nice to hear all music which shares similar sounds, or similar phrases of sounds? Research in the last 10-15 years have developed methods precisely to solve this problem and fall under the umbrella term content-based information retrieval (CBIR) algorithms, or uncovering the relationships of an archive through the information within the content. For images, Google’s Search by Image is a great example which only recently became public. For audio, audioDB and ShaZam are good examples of discovering music through the way it sounds, or the content-based relationships of the audio itself. Though, each of these interfaces present a list of matches to a image or audio query, making exploring the content-based relationships of a specific set of material difficult.

The video above demonstrates interaction with a novel 3D browser … Continue reading...

The post 3D Musical Browser first appeared on http://archive.pkmital.com.

Introduction

My work in audiovisual resynthesis aims to create models of how humans represent and attend to audiovisual scenes. Using pattern recognition of both audio and visual material, these models use large corpora of learned audiovisual material which can be matched to ongoing streams of incoming audio or visual material. The way audio and visual material is stored and segmented within the model is based heavily on neurobiology and behavioral evidence (the details are saved for another post). I have called the underlying model Audiovisual Content-based Information Description/Distortion (or ACID for short).

As an example, a live stream of audio may be matched to a database of learned sounds from recordings of nature, creating a re-synthesis of the audio environment at present using only pre-recorded material from nature itself. These learned sounds may be fragments of a bird chirping, or the sound of footsteps. Incoming sounds of someone talking may then be synthesized using the closest sounding material to that person talking, perhaps a bird chirp or a footstep. Instead of a live stream, one can also re-synthesize a pre-recorded stream. Consider using a database … Continue reading...

The post Intention in Copyright first appeared on http://archive.pkmital.com.

Abstract:
An augmented sonic reality aims to register digital sound content with an existing physical space. Perceptual mappings between an agent in such an environment and the augmented content should be both continuous and effective, meaning the intentions of an agent should be taken into consideration in any affective augmentations. How can an embedded intelligence such as an iPhone equipped with detailed sensor information such as microphone, accelerometer, gyrometer, and GPS readings infer the behaviors of its user in creating affective, realistic, and perceivable augmented sonic realities tied to their situated experiences? Further, what can this augmented domain reveal about our own ongoing sensory experience of our sonic environment?

Keywords: augmented, reality, sonic, enactive, perception, memory, … Continue reading...

The post Augmented Sonic Reality first appeared on http://archive.pkmital.com.

We are in the process of making all of our data freely available for research use. As well, we have also worked on tools for analyzing eye-movements during such dynamic scenes.

CARPE, or more bombastically known as Computational Algorithmic Representation and Processing of Eye-movements, allows one to begin visualizing eye-movement data together with the video data it was tracked with in a number of ways. It currently supports low-level feature visualizations, clustering of eye-movements, model selection, heat-map visualizations, blending, contour visualizations, peek-through visualizations, movie output, binocular data input, and more. The videos shown above on our Vimeo page were all created using this tool. Head over to Google code to check out the source code or download the binary. We are still in the process of stream-lining this process by creating manuals for new users and uploading more of the eye-tracking and video data so … Continue reading...

The post Dynamic Scene Perception Eye-Movement Data Videos and Analysis first appeared on http://archive.pkmital.com.

You can find them here.… Continue reading...

The post Perception Related Videos first appeared on http://archive.pkmital.com.