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Audio keyword generation for sports video analysis ...
https://dl.acm.org/doi/10.1145/1027527.1027702#:~:text=In%20this%20demo%2C%20we%20present%20a%20flexible%20Hidden,and%20employs%20hidden%20states%20transition%20to%20capture%20contexts.
HMM-Based Audio Keyword Generation | SpringerLink
https://link.springer.com/chapter/10.1007/978-3-540-30543-9_71
Experimental results show that, for audio keyword generation, the proposed HMM-based method outperforms the previous hierarchical SVM. Keywords Support Vector Machine Audio Signal Audio Feature Sport Video Soccer Video These keywords were added by machine and not by the authors.
(PDF) HMM-Based Audio Keyword Generation - …
https://www.researchgate.net/publication/220763160_HMM-Based_Audio_Keyword_Generation
that, for audio keyword generation, the proposed HMM-based method outperforms the previous hierarchical SVM. 1 Introduction With the increasing multimedia data …
HMM-based Audio Keyword Generation - ResearchGate
https://www.researchgate.net/profile/Liang-Tien-Chia/publication/220763160_HMM-Based_Audio_Keyword_Generation/links/0046351bfe677e120f000000/HMM-Based-Audio-Keyword-Generation.pdf
HMM-Based Audio Keyword Generation 567 semantic analysis attracts more and more research efforts [1,2,3]. Their works attempt to extract semantic meaning from …
Audio keywords generation for sports video analysis | ACM ...
https://dl.acm.org/doi/10.1145/1352012.1352015
With the help of video shots, the created audio keywords can be used to detect semantic events in sports video by Hidden Markov Model (HMM) learning. Experiments on creating audio keywords and, subsequently, event detection based on …
HMM-Based Photo-Realistic Talking Face Synthesis Using ...
https://file.scirp.org/pdf/JCC_2017082216385517.pdf
The context-dependent HMMs are concate- nated aligned with the label sequence, and an optimal expression parameter se- quence is estimated using the parameter generation algorithm based on maxi- mum likelihood [31], which is described in tionSec 3.4. In the estimation, both the static and dynamic features are taken into account.
EASIER Sampling for Audio Event Identication
http://www.cecs.uci.edu/~papers/icme05/defevent/papers/cr1679.pdf
2.2. HMM-based Audio Event Identication Audio signal exhibits consecutive changes in values over a period of time, where variables may be predicted from ear-lier values. That means, strong context exists in audio data. In consideration of the success of HMM in speech recog-nition, we propose our HMM-based audio event generation system.
SMALL-FOOTPRINT KEYWORD SPOTTING USING DEEP …
https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/42537.pdf
predict the keyword(s) or subword units of the keyword(s) followed by a posterior handling method producing a final confidence score. Keyword recognition results achieve 45% relative improvement with respect to a competitive Hidden Markov Model-based system, while performance in the presence of babble noise shows 39% relative im-provement.
Lecture 6a: Introduction to Hidden Markov Models
https://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/download/lectures/PCB_Lect06_HMM.pdf
HMM is a Markov process that at each time step generates a symbol from some alphabet, Σ, according to emission probability that depends on state. M = (Q, Σ, a,e) Q – finite set of states, say n states ={1,…n} a – n x n transition probability matrix a(i,j) = Pr[q t+1 =j|q t =i] Σ = {σ 1 , …,σ k } e(i,j) == probability of generating symbol σ j
Automatic Speaker Recognition with Limited Data ...
https://dl.acm.org/doi/abs/10.1145/3336191.3371802
HMM-Based Audio Keyword Generation. In Advances in Multimedia Information Processing - PCM 2004, 5th Pacific Rim Conference on Multimedia, Tokyo, Japan, November 30 - December 3, 2004, Proceedings, Part III. 566--574. Hiromu Yakura and Jun Sakuma. 2018. Robust Audio Adversarial Example for a Physical Attack.
Monitoring, profiling and classification ... - ScienceDirect
https://www.sciencedirect.com/science/article/pii/S2352484720313007
HMM-Based audio keyword generation Pacificrim conference on multimedia , Springer , Berlin, Heidelberg ( 2004 ) , pp. 566 - 574 , 10.1007/978-3-540-30543-9_71 CrossRef View Record in Scopus Google Scholar
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