Alan Peevers - Santa Cruz CA Alan Seefeldt - Somerville MA
Assignee:
Creative Technology Ltd.
International Classification:
G06T 1700
US Classification:
345473
Abstract:
A method and system to drive transformations of a visual representation, in real-time, that synchronizes the audio and visual outputs and controls the magnitude of object deformation to be visual pleasing. In one embodiment, sudden increases in the spectral energy are detected to time the initialization of deformations and a smoothed signal is derived from the time varying spectral energy curve to control the magnitude of the deformations.
Audio Driven Texture And Color Deformations Of Computer Generated Graphics
Alan Seefeldt - Santa Cruz CA Alan Peevers - Santa Cruz CA
Assignee:
Creative Technology Ltd. - Singapore
International Classification:
G06T 1500
US Classification:
345475
Abstract:
A technique for changing the appearance of visual images mapped on a 3D object rendered by a computer includes the stepped of remapping texture coordinates to vertices of a 3D object at discrete time intervals and blending the remapped images. The control intervals are triggered by events in monitored signal such as an audio feed or a video stream. The images of the video stream can be the textures mapped to the 3D object.
Automated Acquisition Of Video Textures Acquired From A Digital Camera For Mapping To Audio-Driven Deformable Objects
Alan Peevers - Santa Cruz CA, US Alan Seefeldt - Somerville MA, US
Assignee:
Creative Technology Ltd. - Singapore
International Classification:
G09G005/00
US Classification:
345722, 345582, 345726, 345949, 84464 R
Abstract:
A technique for enhancing an audio-driven computer generated animation includes the step of mapping a video clip generated by a digital camera to an object displayed in the animation. Additionally, the object or the video clip can be deformed when selected events are detected during playback of the video clip.
Alan Seefeldt - Santa Cruz CA, US Alan Peevers - Santa Cruz CA, US
Assignee:
Creative Technology Ltd. - Creative Resource
International Classification:
G06T 15/00
US Classification:
345473
Abstract:
An apparatus and method for generating 3D graphics objects utilizes algorithms to generate the objects when driven by audio events. In one embodiment a “hydra” object has branches that are recursively generated. Parameters used to algorithmically generate the object are controlled by a control signal driven by detected events in an audio signal. Additional algorithms include a phase plot using audio parameters. A generalized system includes an audio analysis block for generating audio control signals utilized by object generation, objects selection and object placement blocks to generate 3D objects.
Device And Method Utilizing A Self-Organizing Visual Analog Representation Of Electronic Media
Mark Buchanan - Santa Cruz CA, US Gaben Chancellor - Santa Cruz CA, US Alan Peevers - Santa Cruz CA, US
International Classification:
H04N 9/64
US Classification:
348604
Abstract:
A method and device for facilitating navigation of digital media to a user are provided. A digital media player, or (“client”) has access to a plurality of digital media records, e.g., a library of digitized musical performances that may be stored in a memory of the device. The client may have access to electronic digital media documents stored in additional devices or memories, and/or via an electronic communications network to a data warehouse. The client enables a user to select an electronic document for playing on the client from a plurality of electronic documents. The client presents two or more visual images to the user, wherein each visual image represents a discrete electronic digital document, e.g., a CD jewelcase or a DVD package. The software directs the user to select a visual image, in which the images that are subsequently presented to the user may depend upon previous user selections.
A method and apparatus for vocally entering acoustic data and producing an output. In one embodiment, a note preset is identified and selected according to the vocal input signal, and auxiliary note information is also extracted from the vocal input signal. The auxiliary note information is used to generate synthesis engine parameters that modify the note preset to provide a complex note output. In another embodiment, feature vectors of note segments are used to select a preset file representing a particular instrument from a library of instrument preset files. A note preset is selected from the instrument preset file according to the note segment to create an output corresponding to the selected instrument or instrument group.
A system and method for modifying a subportion of information contained in an audio, such as magnitude information, without substantially effecting the remaining information contained therein, such a phase information. An incoming audio signal is segmented into a sequence of overlapping windowed DFT representations, during an analysis step, and during a synthesis step the DFT representations are converted back to a time domain signal. Each of the DFT representations consists of a plurality of frequency components obtained during a period of time. Each of the frequency components is associated with a unique increment of the period. Subsequent to the analysis step, but before the synthesis step, the frequency components of the DFT representations are re-mapped so as to have a differing temporal relationship with respect to the increments of the period of time.
Machine Learning To Recognize Key Moments In Audio And Video Calls
- Redmond WA, US Alan Wesley Peevers - Santa Cruz CA, US
International Classification:
G06N 99/00
Abstract:
Various embodiments provide an ability to automatically capture audio and/or video during a communication exchange between participants. At times, the automatic capture can be triggered when one or more characteristics are identified and/or observed during the communication exchange. In some cases, analyzing the communication exchange for characteristic(s) is based on previous input. Some embodiments train a machine-learning algorithm on desired characteristic(s) using multiple user-initiated video and/or audio clips. Alternately or additionally, some embodiments identify characteristic(s) in a communication exchange based on other properties not provided by user-input.
Adobe
Senior Software Engineer
Peevers Consulting Services
Owner
Skype May 2012 - Sep 2017
End To End Architect - User Experiences
Peevers Consulting Services Jan 2004 - May 2012
President
Avid Technology Mar 2009 - Apr 2012
Project Leader - Multi-Core Audio Mixer Engine
Education:
University of California, Berkeley 1991 - 1994
Master of Science, Masters, Electrical Engineering
Massachusetts Institute of Technology 1978 - 1982
Bachelors, Bachelor of Science, Electrical Engineering, Electrical Engineering and Computer Science, Computer Science
Brookfield High School
Skills:
3D Graphics Digital Signal Processors Embedded Systems Hardware Architecture Programming Firmware Device Drivers Arm Embedded Software Usb Embedded Linux Digital Signal Processing Debugging Rtos Fpga Signal Processing Python Software Development Software Design C Multithreading Software Engineering System Architecture Perforce Algorithms Object Oriented Design System Design Engineering Management C++
Interests:
Animals The Daily Show Barack Obama Esalen Institute Harbor Cafe Microsoft Lync Org Brooklyn Museum Safeway Santa Cruz Igor Stravinsky Joni Mitchell Consumer Media Systems Music Synthesis Digital Audio Research Hi Tech Management Augmented Reality Nui Consumer Reports Machine Learning Pbs 350 Virtual Reality Skype Dalai Lama Techcrunch