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Multimedia Interfaces What is a multimedia interface – Most anything where users do not just interact with text – E.g., audio, speech, images, faces, video,

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Presentation on theme: "Multimedia Interfaces What is a multimedia interface – Most anything where users do not just interact with text – E.g., audio, speech, images, faces, video,"— Presentation transcript:

1 Multimedia Interfaces What is a multimedia interface – Most anything where users do not just interact with text – E.g., audio, speech, images, faces, video, sensor data, …

2 Working with Multimedia Symbolic vs. non-symbolic content – How can users search and browse for the content they need? – What is represented and what is not? – Important that interface design be appropriate to the particular content processing techniques Static vs. dynamic content – How can users locate particular states within a piece of content? – Need visualizations that enable state/segment-based indexing and visualization

3 General Audio Mapping audio cues to events – Recognizing sounds related to particular events (e.g. gunshot, falling, scream) Mapping events to audio cues – Audio debugger to speed up stepping through code Spatialized audio – Provides additional geographic/navigational channel – Example: Michael Joyce’s Interactive Central Park

4 Spatialized Audio Spatialized audio is easier when assuming headphones because of control Head-related transfer function (HRTF) – Difference in timing and signal strength determine how we identify position of sound Beamforming – Timing for constructive interference to create stronger signal at desired location Crosstalk Cancellation – Destructive interference to remove parts of signal at desired location

5 Audio Signal Analysis Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) – Transforms commonly used on audio signals – Allow for analysis of frequency features across time (e.g. power contained in a frequency interval) – FFTs have equal sized windows where wavelets can vary based on frequency Mel-frequency cepstral coeffients (MFCC) – Based on FFTs – Maps results into bands approximating human auditory system

6 Speech Speaker segmentation – Identify when a change in speaker occurs – Useful for basic indexing or summarization of speech content Speaker identification – Identify who is speaking during a segment – Enables search (and other features) based on speaker Speech recognition – Identify the content of speech

7 Speech Recognition Start by segmenting utterances and characterizing phonemes – Use gaps to segment – Group segments into words Limited vocabulary of commands – Classifiers for limited vocabulary (HMMs) Continuous speech – Language models for disambiguation – Speaker dependent or not

8 Music Music processing can support a variety of activities Composition – From traditional to interactive Selection – Example: iTunes, Pandora, – Use for shared spaces Playback – Interactive playback, social playback Management & Summarization – Example: MusicWiz Games – Guitar Hero, Rockband, etc.

9 MobiLenin Enable interaction with music in a public space – Not karaoke Voting like in many pub/bar games Audience can affect which version of music and video is shown

10 MusicWiz Metadata Module Audio Signal Module Lyrics Module Worksp. Express. Module Artist Module Relatedness Table Inference Engine Workspace Status Related Song Titles Music Collection Songs & Metadata Songs MusicWiz Interface Lyrics Statistics of Artist Similarity Internet Relatedness Assessment Sim. Values Music management environment that combines: –explicit information –implicit information –non-verbal expression of personal interpretation Two basic components: –interface for interacting with the music collection –inference engine for assessing music relatedness

11 Image Processing: Color Color histograms – how much of each color is in image – Probability of a pixel in the image being a particular color Color correlograms – how close colors are to each other in image – Probability of finding a pixel of a particular color at a specific distance from a pixel of a known color

12 Image/Video Processing: Subdividing Region subdivision – Sometimes we subdivide images into regions – Spread observed features at edges for more continuous model Temporal subdivision – Video is subdivided into segments – Spread features into neighboring segments

13 Image Processing: Foreground Background Separation Background Modeling – Convert to greyscale – Dynamic model (to cope with changes in signer body position and lighting) BP t =.96 * BP (t-1) +.04 P Foreground object detection – Pixels different from background model by more than a threshold are foreground pixels – Spatial filter removes regions of foreground pixels smaller than a minimum threshold Face location to determine position of foreground relative to the face Videos without a single main face are not considered as potential SL videos 13

14 Image Processing: Other Features Edge detection – Sobel filter Object and Face detection – Skintone models Face recognition Open Source Computer Vision (OpenCV)

15 MediaGLOW: Interpreting User Action Evolving Notion of Similarity via User Expression – Photos presented in a graph-based workspace with “springs” between each pair of photos. – Lengths of springs is initially based on a default distance metric based on their time, location, tags, or visual features. – Users can pin photos in place and create piles of photos. – Distance metric to piles change as new members are added, resulting in the dynamic layout of unpinned photos in the workspace.

16 DOTS: Supporting Use of Surveillance Video The problem – Number and size of surveillance systems are increasing but human attention is limiting factor Approach – Provide summaries of action – Build interfaces knowing limits of automation

17 DOTS: The Main Interface Components – Rotating camera bank with activity graphs – Mixed-initiative main viewer – Map with tracking data – Timeline with automatic events

18 DOTS: Tracking Layout Difficulty in tracking is that camera views are often similar Tracking layout places cameras around the main viewer to aid tracking Study showed significant improvement in tracking success over traditional viewer In either layout, map can be used to find activity near a location and time.

19 HyperHitchcock: Interactive Video Issue – Vision: Seamlessly interact with characters in the show – Reality: Difficult to author even simple interactive videos Today, video is included within pages of content but links between playing videos are not common.

20 Support for Hypervideo Authoring Links in video can lead to other video segments – Short main video with branches providing additional detail – Hyperlinks to branches just like in Web pages – Making of a scene in a movie, biography of an actor, different camera angle General hypervideo difficult to author – Simple hypervideo format with only a single active link Novel approach: use automatic video analysis, create an easy-to-use interface, and support simple hypervideo format

21 Hierarchical Video with Links Video sequences are represented as a containment hierarchy of video elements – Elements are video clips or composites grouping other video elements – Elements are played in sequence Each element can be link anchor or link destination Anchor for innermost element is available while element is playing After link destination video is played, play-back continues at the link anchor

22 Detail-on-demand Links Any video clip or composite can be link anchor or link destination Optional link offsets into destination Links have labels Link return behaviors control the purpose of the link – Play from where the viewer left the video – Play from the end of the source anchor sequence – Play from beginning of the source anchor sequence – Stop playback Different behaviors for destination completion or aborted playback

23 Hyper-Hitchcock Editor Hyper-Hitchcock evolved from Hitchcock video editor Video clips grouped in piles by similarity (e.g., recording time) Workspace to arrange clips – Resize keyframes to trim clips – Clips ordered as horizontal or vertical lists – Place links between clips – Group clips into composites Tree view to visualize containment hierarchy of composites

24 Trimming Clips in the Workspace Best five seconds of clip selected by default Resizing keyframe changes length of clip – Picks the best portion around initial five-second portion – Start and end can jump to sentence boundary silence Clip start and/or end can be locked in timeline Locked ends can be dragged Audio energy visualized in timeline to spot words and sentences

25 Attaching Links to Clips and Composites Link anchors and destinations can be clips, composites, or elements inside composites Color-coding and position indicates link attachment in workspace Links in and out of composite Blue: attached to composite Red: attached to element Dashed: between composite and element

26 Hypervideo Player Video player with controls for following and returning from links Several improvements based on user feedback – First version indicated links in timeline and showed the label for the active link – Next version showed labels in timeline – Current version includes keyframes for active link and for link history User study suggests further improvements

27 Today’s Topics General Audio – Audio cues, spatialized audio Speech – Segmentation, speaker id, recognition Music – Interactive music, summarization, organization Image and video processing – Color-oriented representations – Region and temporal segmentation – Foreground-background separation – Edge and face detection Image and video applications – MediaGlow – image selection – DOTS – surveillance – HyperHitchcock – interactive video

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