PPT-Audio Feature Representations

Detecting Semantic Concepts In Consumer Videos Using Audio Junwei Liang Qin Jin Xixi He Gang Yang Jieping Xu Xirong Li Multimedia Computing Lab School of Information . He Wang, . Dimitrios Lymberopoulos. , Jie . L. iu. Local Search Experience. . Static/Stale Experience. Ratings/Reviews. Images. Location/Phone. URL. Is it crowded . now. ?. What music is it playing . The full standard initiative is located at . www.voicebiometry.org. Quick description. Standard manual with detailed description and a quick user guide to…. The reference demo package. Contains full speaker-recognition (demo) pipeline. learning and prediction. Jongmin. Kim. Seoul National University. Problem statement. Predicting outcome of surgery. Predicting outcome of surgery. Ideal approach. . . . .. ?. Training Data. Predicting outcome. Daniel Lowd. University of Oregon. April 20, 2015. Caveats. The purpose of this talk is to inspire meaningful discussion.. I may be completely wrong.. My background:. Markov logic networks, probabilistic graphical models. The full standard initiative is located at . www.voicebiometry.org. Quick description. Standard manual with detailed description and a quick user guide to…. The reference demo package. Contains full speaker-recognition (demo) pipeline. via Brain simulations . Andrew . Ng. Stanford University. Adam Coates Quoc Le Honglak Lee Andrew Saxe Andrew Maas Chris Manning Jiquan Ngiam Richard Socher Will Zou . Thanks to:. Commercial Detection in Videos of TV Show. Zheyun. Feng. TABLE OF CONTENTS. Re-organize Programming Framework. Frame Feature Improvement. New Image Feature. Re-packaging Audio Packet. New Audio Feature. Yuchen Fan, Matt Potok, Christopher Shroba. Motivation. Text-to-Speech. Accessibility features for people with little to no vision, or people in situations where they cannot look at a screen or other textual source. Yu-Gang . Jiang. School of Computer Science. Fudan University. Shanghai, China. ygj@fudan.edu.cn. ACM ICMR 2012, Hong Kong, June 2012. S. peeded . Up. . E. vent . R. ecognition. ACM International Conference on Multimedia Retrieval (ICMR), Hong Kong, China, Jun. 2012.. Motivation. Text-to-Speech. Accessibility features for people with little to no vision, or people in situations where they cannot look at a screen or other textual source. Natural language interfaces for a more fluid and natural way to interact with computers. Das (UIUC). , . Nikita Borisov (UIUC. ),. Matthew Caesar (UIUC). Do You Hear What I Hear? Fingerprinting Smart Devices Through Embedded Acoustic Components. 1. CCS . 2014. November 4, 2014. 2. Smartphone Usage. Das (UIUC). , . Nikita Borisov (UIUC. ),. Matthew Caesar (UIUC). Do You Hear What I Hear? Fingerprinting Smart Devices Through Embedded Acoustic Components. 1. CCS . 2014. November 4, 2014. 2. Smartphone Usage. Matthew Black, . Athanasios. . Katsamanis. , Chi-Chun Lee, Adam C. . Lammert. , Brian R. . Baucom. , Andrew Christensen, Panayiotis G. Georgiou, and . Shrikanth. Narayanan. September 29, 2010. Automatic Classification of Married Couples' Behavior using Audio Features. Grows out of DSP and speech recognition research. Feature detection mostly from . Fast Fourier Transforms (FFT) . and . Mel Frequency . Cepstral. Coefficients (MFCC). 1. Music Digital Audio. 2. http://en.wikipedia.org/wiki/Digital_audio.