On solvable Leibniz algebras whose nilradical has characteristic sequence (m1,m2,m3)

2018 ◽  
Vol 2018 (3) ◽  
pp. 4-17
Author(s):  
K.K. Abdurasulov ◽  
Drew Horton ◽  
U.X. Mamadaliyev
Keyword(s):  
Characteristic Sequence ◽  
Leibniz Algebras

A KFCM and SIFT Based Matching Approach to Similarity Retrieval of Images

2009 ◽  
Author(s):  
Pengyi Hao ◽  
Youdong Ding ◽  
Yuchun Fang ◽  
Ranran Zhang ◽  
Shuhan Wei
Keyword(s):  
Similarity Retrieval

MULTI-VERSION MUSIC SEARCH USING ACOUSTIC FEATURE UNION AND EXACT SOFT MAPPING

2009 ◽  
Vol 03 (02) ◽  
pp. 209-234 ◽  
Author(s):  
YI YU ◽  
KAZUKI JOE ◽  
VINCENT ORIA ◽  
FABIAN MOERCHEN ◽  
J. STEPHEN DOWNIE ◽  
...  
Keyword(s):  
Collision Probability ◽  
Acoustic Feature ◽  
Music Retrieval ◽  
Feature Sets ◽  
Soft Mapping ◽  
Audio Features ◽  
Music Audio ◽  
Locality Sensitive Hash ◽  
Principle Of Similarity ◽  
Search Quality

Research on audio-based music retrieval has primarily concentrated on refining audio features to improve search quality. However, much less work has been done on improving the time efficiency of music audio searches. Representing music audio documents in an indexable format provides a mechanism for achieving efficiency. To address this issue, in this work Exact Locality Sensitive Mapping (ELSM) is suggested to join the concatenated feature sets and soft hash values. On this basis we propose audio-based music indexing techniques, ELSM and Soft Locality Sensitive Hash (SoftLSH) using an optimized Feature Union (FU) set of extracted audio features. Two contributions are made here. First, the principle of similarity-invariance is applied in summarizing audio feature sequences and utilized in training semantic audio representations based on regression. Second, soft hash values are pre-calculated to help locate the searching range more accurately and improve collision probability among features similar to each other. Our algorithms are implemented in a demonstration system to show how to retrieve and evaluate multi-version audio documents. Experimental evaluation over a real "multi-version" audio dataset confirms the practicality of ELSM and SoftLSH with FU and proves that our algorithms are effective for both multi-version detection (online query, one-query vs. multi-object) and same content detection (batch queries, multi-queries vs. one-object).

Deep learning-based similarity retrieval in clinical 3D gait analysis

2021 ◽  
Vol 90 ◽  
pp. 127-128
Author(s):  
P. Krondorfer ◽  
D. Slijepčević ◽  
F. Unglaube ◽  
A. Kranzl ◽  
C. Breiteneder ◽  
...  
Keyword(s):  
Deep Learning ◽  
Gait Analysis ◽  
Similarity Retrieval

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.

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