scholarly journals A hierarchical Bayesian language model based on Pitman-Yor processes

2006 ◽  
Author(s):  
Yee Whye Teh
Keyword(s):  
Language Model ◽  
Hierarchical Bayesian ◽  
Model Based

scholarly journals Advantages and insights from a hierarchical Bayesian growth and dynamics model based on salmonid electrofishing removal data

2019 ◽  
Vol 392 ◽  
pp. 8-21 ◽  
Author(s):  
Christophe Laplanche ◽  
Pedro M. Leunda ◽  
Laurie Boithias ◽  
José Ardaíz ◽  
Francis Juanes
Keyword(s):  
Hierarchical Bayesian ◽  
Dynamics Model ◽  
Model Based

scholarly journals Chord-aware automatic music transcription based on hierarchical Bayesian integration of acoustic and language models

2018 ◽  
Vol 7 ◽  
Author(s):  
Yuta Ojima ◽  
Eita Nakamura ◽  
Katsutoshi Itoyama ◽  
Kazuyoshi Yoshii
Keyword(s):  
Latent Variables ◽  
Language Model ◽  
Language Models ◽  
Sequential Dependency ◽  
Acoustic Model ◽  
Hierarchical Bayesian ◽  
Generative Process ◽  
Music Transcription ◽  
Automatic Music Transcription ◽  
Music Audio

This paper describes automatic music transcription with chord estimation for music audio signals. We focus on the fact that concurrent structures of musical notes such as chords form the basis of harmony and are considered for music composition. Since chords and musical notes are deeply linked with each other, we propose joint pitch and chord estimation based on a Bayesian hierarchical model that consists of an acoustic model representing the generative process of a spectrogram and a language model representing the generative process of a piano roll. The acoustic model is formulated as a variant of non-negative matrix factorization that has binary variables indicating a piano roll. The language model is formulated as a hidden Markov model that has chord labels as the latent variables and emits a piano roll. The sequential dependency of a piano roll can be represented in the language model. Both models are integrated through a piano roll in a hierarchical Bayesian manner. All the latent variables and parameters are estimated using Gibbs sampling. The experimental results showed the great potential of the proposed method for unified music transcription and grammar induction.

scholarly journals A trigger language model-based IR system

2004 ◽  
Author(s):  
Zhang Jun-lin ◽  
Sun Le ◽  
Qu Wei-min ◽  
Sun Yu-fang
Keyword(s):  
Language Model ◽  
Model Based

A Hierarchical Bayesian Model-Based Uncertainty Analysis for Tsunami Heights along Shorelines in Korea

2016 ◽  
Vol 75 (sp1) ◽  
pp. 1157-1161 ◽  
Author(s):  
Hyun-Han Kwon ◽  
Jin-Young Kim ◽  
Byoung Han Choi ◽  
Yong-Sik Cho
Keyword(s):  
Uncertainty Analysis ◽  
Bayesian Model ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Model Based

Hierarchical Bayesian Model Based on Robust Fixed Rank Filter for Fusing MODIS SST and AMSR-E SST

2019 ◽  
Vol 85 (2) ◽  
pp. 119-131 ◽  
Author(s):  
Yuxin Zhu ◽  
Emily Lei Kang ◽  
Yanchen Bo ◽  
Jinzong Zhang ◽  
Yuexiang Wang ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Model Based ◽  
Fixed Rank

Mobile Geographic Web Search Personalization with Language Model

2013 ◽  
Vol 303-306 ◽  
pp. 1420-1425
Author(s):  
Qiang Pu ◽  
Ahmed Lbath ◽  
Da Qing He
Keyword(s):  
Query Expansion ◽  
Web Search ◽  
Mixed Model ◽  
Topic Model ◽  
Language Model ◽  
Mobile Users ◽  
Information Need ◽  
User Interest ◽  
Modeling Framework ◽  
Model Based

Mobile personalized web search has been introduced for the purpose of distinguishing mobile user's personal different search interest. We first take the user's location information into account to do a geographic query expansion, then present an approach to personalizing web search for mobile users within language modeling framework. We estimate a user mixed model estimated according to both activated ontological topic model-based feedback and user interest model to re-rank the results from geographic query expansion. Experiments show that language model based re-ranking method is effective in presenting more relevant documents on the top retrieved results to mobile users. The main contribution of the improvements comes from the consideration of geographic information, ontological topic information and user interests together to find more relevant documents for satisfying their personal information need.

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