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Computer Assisted Composition with Recurrent Neural Networks [article]

Christian Walder, Dongwoo Kim
2017 arXiv   pre-print
Sequence modeling with neural networks has lead to powerful models of symbolic music data.  ...  We address the problem of exploiting these models to reach creative musical goals, by combining with human input.  ...  Concretely, here we adopt the Long Short-Term Memory (LSTM) based neural network sequence model (Hochreiter and Schmidhuber, 1997) .  ... 
arXiv:1612.00092v2 fatcat:dfx6w3zsbrhtbgbtqq7nw5htsm

Deep Learning for Semantic Composition

Xiaodan Zhu, Edward Grefenstette
2017 Proceedings of ACL 2017, Tutorial Abstracts  
Next, we discuss composition models that integrate multiple architectures of neural networks. We also discuss semantic composition and decomposition (Turney, 2014) .  ...  We cover the generic ideas behind neural network-based semantic composition and dive into the details of three typical composition architectures: the convolutional composition models (Kalchbrenner et  ... 
doi:10.18653/v1/p17-5003 dblp:conf/acl/ZhuG17 fatcat:svm6zwik4jdljfm4dcjpw77ti4

Hiligaynon – Cebuano Sentence Translator using Recurrent Neural Networks

Sean Michael A. Cadigal, Christine F. Peña, Christian P. Gelbolingo, Anthonette D. Cantara
2018 Zenodo  
The study aimed to create a translation model for Hiligaynon to Cebuano by applying recurrent artificial neural networks with long short-term memory.  ...  Two neural networks were developed, one for encoding source sentences and one for decoding target sentences in a manner following sequence-to-sequence learning.  ...  to ours and assisting us in learning the concepts of artificial neural networks in addition to helping train our translation model, to Marianne Mithun for helping gather related literature relating to  ... 
doi:10.5281/zenodo.5211079 fatcat:xb3nemtu4zcenp735ijcqat5te

Pedestrian detection using Doppler radar and LSTM neural network

Mussyazwann Azizi Mustafa Azizi, Mohammad Nazrin Mohd Noh, Idnin Pasya, Ahmad Ihsan Mohd Yassin, Megat Syahirul Amin Megat Ali
2020 IAES International Journal of Artificial Intelligence (IJ-AI)  
The traces are then fed to LSTM neural network for training, validation and testing.  ...  <span lang="EN-US">Integration of radar systems as primary sensor with deep learning algorithms in driver assist systems is still limited.  ...  Pedestrian detection using LSTM neural network LSTM is an improvement of the recurrent neural network (RNN) used for modelling sequential data.  ... 
doi:10.11591/ijai.v9.i3.pp394-401 fatcat:6derfdmdxrb3jdlansnfcrqmvu

Case Studies for Applications of Elman Recurrent Neural Networks [chapter]

Elif Derya, Mustafa beyli
2008 Recurrent Neural Networks  
Introduction Artificial neural networks (ANNs) are computational modeling tools that have recently emerged and found extensive acceptance in many disciplines for modeling complex realworld problems.  ...  features (Lyapunov exponents), and a feature classifier that outputs the class based on the composite features (recurrent neural networks -RNNs).  ...  Elman recurrent neural networks for detection of electrocardiographic changes in partial epileptic patients The aim of this study is to evaluate the diagnostic accuracy of the RNNs with composite features  ... 
doi:10.5772/5550 fatcat:74atg2hvkzckvboq6wciqpqp6u

AIA: Artificial intelligence for art

Robert B. Lisek
2018 EVA London 2018  
Recurrent neural network. Reinforcement learning.  ...  A recurrent neural network (RNN) is a class of artificial neural network where connections between units form a directed cycle, meaning that Recurrent Neural Network contains feedback connections, connections  ...  Reinforcement Learning and integrate it with deep learning, recurrent networks.  ... 
doi:10.14236/ewic/eva2018.5 dblp:conf/eva/Lisek18 fatcat:wcpjqcrm2zbpvdtnpt63zls2um

Editorial: Music and AI

Alexandra Bonnici, Roger B. Dannenberg, Steven Kemper, Kenneth P. Camilleri
2021 Frontiers in Artificial Intelligence  
Editorial on the Research Topic Music and AI Computer algorithms have been shaping the music scene since the 1950s.  ...  Artificial intelligence, machine learning and computational methods have left their mark not only on the way that music is composed and performed but also on the adoption of new musical notations; different  ...  , and an internal sequence-prediction algorithm based on a recurrent neural network and trained on human performances.  ... 
doi:10.3389/frai.2021.651446 pmid:33733233 pmcid:PMC7905163 fatcat:sey27hy3kfcu3gxtkbaqp3nfiy

Recurrent Neural Network with Human Simulator Based Virtual Reality [chapter]

Yousif I. Al Mashhadany
2012 Recurrent Neural Networks and Soft Computing  
, tapped-delayed feedforward networks and to the fully recurrent networks are: www.intechopen.com Recurrent Neural Network with Human Simulator Based Virtual Reality 91 www.intechopen.com Recurrent Neural  ...  Networks and Soft Computing 92 The name Fully Recurrent Neural Network for this network type is proposed by [Kasper e.a., 1999].  ...  Recurrent Neural Networks and Soft Computing 96 Fig. 5.  ... 
doi:10.5772/35538 fatcat:ockq2fa2mzhvpftn2nwheq5ag4

Deep Echo State Network (DeepESN): A Brief Survey [article]

Claudio Gallicchio, Alessio Micheli
2020 arXiv   pre-print
The study of deep recurrent neural networks (RNNs) and, in particular, of deep Reservoir Computing (RC) is gaining an increasing research attention in the neural networks community.  ...  At the same time, the study of DeepESNs allowed to shed light on the intrinsic properties of state dynamics developed by hierarchical compositions of recurrent layers, i.e. on the bias of depth in RNNs  ...  Introduction In the last decade, the Reservoir Computing (RC) paradigm [1, 2] has attested as a state-of-the-art approach for the design of efficiently trained Recurrent Neural Networks (RNNs).  ... 
arXiv:1712.04323v4 fatcat:eoletuxluveinaiycqdirczsyu

Neural networks in art, sound and design

Juan Romero, Penousal Machado
2020 Neural computing & applications (Print)  
autoencoders and recurrent neural networks-for generative tasks in nongame-related contexts; the increasing use of deep learning in the procedural content generation.  ...  In recent years, the growth of the scientific community devoted to computational creativity and the developments in the field of machine learning-along with the increasing computational power-gave rise  ... 
doi:10.1007/s00521-020-05444-y fatcat:aszni43uo5cenf5jurlmxvbnaa

Performance Analysis of the Deep Learning Method for Medical Cases

Dian Pratiwi, Anung B. Ariwibowo, Dimmas Mulya
2021 International Journal of Computer Applications  
Deep Learning method is split up into several derived methods, that methods are Deep Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks.  ...  There are many methods used in research to assist in the process of diagnosing a patient's disease. Deep learning method is the one that is used.  ...  Recurrent Neural Network In R.  ... 
doi:10.5120/ijca2021921275 fatcat:zg7clpdfdre73iant7d7fypw74

Speech To Semantics: Improve ASR and NLU Jointly via All-Neural Interfaces [article]

Milind Rao, Anirudh Raju, Pranav Dheram, Bach Bui, Ariya Rastrow
2020 arXiv   pre-print
We then present a compositional model, which generates the transcript using the Listen Attend Spell ASR system and then extracts interpretation using a neural NLU model.  ...  Finally, we contrast these methods to a jointly trained end-to-end joint SLU model, consisting of ASR and NLU subsystems which are connected by a neural network based interface instead of text, that produces  ...  Includes ASR subsystem, Neural NLU subsystem, Compositional pipeline and joint pipeline tion networks [7] was the first all neural E2E ASR model that trained a Recurrent Neural Network (RNN) on audio  ... 
arXiv:2008.06173v1 fatcat:m7gkwdk6pfev5drhnssu54dify

Speech to Semantics: Improve ASR and NLU Jointly via All-Neural Interfaces

Milind Rao, Anirudh Raju, Pranav Dheram, Bach Bui, Ariya Rastrow
2020 Interspeech 2020  
We then present a compositional model, which generates the transcript using the Listen Attend Spell ASR system and then extracts interpretation using a neural NLU model.  ...  Finally, we contrast these methods to a jointly trained end-to-end joint SLU model, consisting of ASR and NLU subsystems which are connected by a neural network based interface instead of text, that produces  ...  ASR model that trained a Recurrent Neural Network (RNN) on audio input features with a transcript label sequence of a different length by considering all possible alignments between inputs and labels.  ... 
doi:10.21437/interspeech.2020-2976 dblp:conf/interspeech/RaoRDBR20 fatcat:7jgh453cuvbpdmuiihddioh26m

Table of contents

2018 IEEE Transactions on Cognitive and Developmental Systems  
Nagai 1043 Recognition of Visually Perceived Compositional Human Actions by Multiple Spatio-Temporal Scales Recurrent Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Cosman 1143 Brain-Inspired Motion Learning in Recurrent Neural Network With Emotion Modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tcds.2018.2882932 fatcat:xhvasdw5ozgepbvakk3dtlo7o4

A Laptop Ensemble Performance System using Recurrent Neural Networks

Rohan Proctor, Charles Patrick Martin
2020 Proceedings of the International Conference on New Interfaces for Musical Expression  
The popularity of applying machine learning techniques in musical domains has created an inherent availability of freely accessible pre-trained neural network (NN) models ready for use in creative applications  ...  the assisting generative models.  ...  Acknowledgments We wish to thank the ANU Laptop Ensemble for participating in live performances with our system.  ... 
doi:10.5281/zenodo.4813481 fatcat:aqpsbtqulbckbprtwn2zqp2loy
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