scholarly journals Exploiting Temporal Dependencies for Cross-modal Music Piece Identification

2021 ◽  
Author(s):  
Luis Carvalho ◽  
Gerhard Widmer
Keyword(s):  
Music Piece ◽  
Temporal Dependencies ◽  
Modal Music

scholarly journals Adaptive Method for Modeling of Temporal Dependencies between Fields of Vision in Multi-Camera Surveillance Systems

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1303
Author(s):  
Karol Lisowski ◽  
Andrzej Czyżewski
Keyword(s):  
Statistical Data ◽  
Input Parameter ◽  
Gaussian Mixture ◽  
Adaptive Method ◽  
Transition Time ◽  
Surveillance Systems ◽  
Modified Particle Swarm Optimization ◽  
Novel Approach ◽  
Transition Times ◽  
Temporal Dependencies

A method of modeling the time of object transition between given pairs of cameras based on the Gaussian Mixture Model (GMM) is proposed in this article. Temporal dependencies modeling is a part of object re-identification based on the multi-camera experimental framework. The previously utilized Expectation-Maximization (EM) approach, requiring setting the number of mixtures arbitrarily as an input parameter, was extended with the algorithm that automatically adapts the model to statistical data. The probabilistic model was obtained by matching to the histogram of transition times between a particular pair of cameras. The proposed matching procedure uses a modified particle swarm optimization (mPSO). A way of using models of transition time in object re-identification is also presented. Experiments with the proposed method of modeling the transition time were carried out, and a comparison between previous and novel approach results are also presented, revealing that added swarms approximate normalized histograms very effectively. Moreover, the proposed swarm-based algorithm allows for modelling the same statistical data with a lower number of summands in GMM.

scholarly journals deepsing: Generating sentiment-aware visual stories using cross-modal music translation

2021 ◽  
Vol 164 ◽  
pp. 114059
Author(s):  
Nikolaos Passalis ◽  
Stavros Doropoulos
Keyword(s):  
Modal Music

Algorithmic music generation by harmony recombination with genetic algorithm

2021 ◽  
pp. 1-13
Author(s):  
Omar Lopez-Rincon ◽  
Oleg Starostenko ◽  
Alejandro Lopez-Rincon
Keyword(s):  
Genetic Algorithm ◽  
New Music ◽  
Novel Approach ◽  
Time Patterns ◽  
Algorithmic Music ◽  
Long Time ◽  
Music Generation ◽  
Music Piece ◽  
Short Time ◽  
Algorithmic Music Composition

Algorithmic music composition has recently become an area of prestigious research in projects such as Google’s Magenta, Aiva, and Sony’s CSL Lab aiming to increase the composers’ tools for creativity. There are advances in systems for music feature extraction and generation of harmonies with short-time and long-time patterns of music style, genre, and motif. However, there are still challenges in the creation of poly-instrumental and polyphonic music, pieces become repetitive and sometimes these systems copy the original files. The main contribution of this paper is related to the improvement of generating new non-plagiary harmonic developments constructed from the symbolic abstraction from MIDI music non-labeled data with controlled selection of rhythmic features based on evolutionary techniques. Particularly, a novel approach for generating new music compositions by replacing existing harmony descriptors in a MIDI file with new harmonic features from another MIDI file selected by a genetic algorithm. This allows combining newly created harmony with a rhythm of another composition guaranteeing the adjustment of a new music piece to a distinctive genre with regularity and consistency. The performance of the proposed approach has been assessed using artificial intelligent computational tests, which assure goodness of the extracted features and shows its quality and competitiveness.

scholarly journals Cross-Modal Music Retrieval and Applications: An Overview of Key Methodologies

2019 ◽  
Vol 36 (1) ◽  
pp. 52-62 ◽  
Author(s):  
Meinard Mueller ◽  
Andreas Arzt ◽  
Stefan Balke ◽  
Matthias Dorfer ◽  
Gerhard Widmer
Keyword(s):  
Music Retrieval ◽  
Modal Music

scholarly journals Exploration of neural correlates of movement intention based on characterisation of temporal dependencies in electroencephalography

PLoS ONE ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. e0193722 ◽  
Author(s):  
Maitreyee Wairagkar ◽  
Yoshikatsu Hayashi ◽  
Slawomir J. Nasuto
Keyword(s):  
Neural Correlates ◽  
Movement Intention ◽  
Temporal Dependencies

Exploring deep learning approaches to predict hourly evolution of surge levels

2021 ◽  
Author(s):  
Timothy Tiggeloven ◽  
Anaïs Couasnon ◽  
Chiem van Straaten ◽  
Sanne Muis ◽  
Philip Ward
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Storm Surge ◽  
Reference Model ◽  
Flood Hazard ◽  
Tide Gauge ◽  
Neural Network Models ◽  
Coastal Flood ◽  
Wide Range ◽  
Temporal Dependencies

<p>In order to better understand current coastal flood risk, it is critical to be able to predict the characteristics of non-tidal residuals (from here on referred to as surges), such as their temporal variation and the influence of coastal complexities on the magnitude of storm surge levels. In this study, we use an ensemble of Deep Learning (DL) models to predict hourly surge levels using four different types of neural networks and evaluate their performance. Among deep learning models, artificial neural networks (ANN) have been popular neural network models for surge level prediction, but other DL model types have not been investigated yet. In this contribution, we use three DL approaches - CNN, LSTM, and a combined CNN-LSTM model- , to capture temporal dependencies, spatial dependencies and spatio-temporal dependencies between atmospheric conditions and surges for 736 tide gauge locations. Using the high temporal and spatial resolution atmospheric reanalysis datasets ERA5 from ECMWF as predictors, we train, validate and test surge based on observed hourly surge levels derived from the GESLA-2 dataset. We benchmark our results obtained with DL to those provided by a simple probabilistic reference model based on climatology. This study shows promising results for predicting the temporal evolution of surges with DL approaches, and gives insight into the capability to gain skill using DL approaches with different Architectures for surge prediction. We therefore foresee a wide range of advantages in using DL models for coastal applications: probabilistic coastal flood hazard assessment, rapid prediction of storm surge estimates, future predictions of surge levels.</p>

A Study on the Form of Modal Music used in K-Pop

2019 ◽  
Vol 13 (6) ◽  
pp. 25-32
Author(s):  
Jin-Seon Kim ◽  
Jae-Youn Chung
Keyword(s):  
Modal Music
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