scholarly journals Sample-level sound synthesis with recurrent neural networks and conceptors

2019 ◽  
Vol 5 ◽  
pp. e205 ◽  
Author(s):  
Chris Kiefer
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Synthesis Method ◽  
Network Models ◽  
Sound Synthesis ◽  
Boolean Logic ◽  
Training Data ◽  
Sound Design ◽  
Echo State Networks ◽  
Granular Synthesis

Conceptors are a recent development in the field of reservoir computing; they can be used to influence the dynamics of recurrent neural networks (RNNs), enabling generation of arbitrary patterns based on training data. Conceptors allow interpolation and extrapolation between patterns, and also provide a system of boolean logic for combining patterns together. Generation and manipulation of arbitrary patterns using conceptors has significant potential as a sound synthesis method for applications in computer music but has yet to be explored. Conceptors are untested with the generation of multi-timbre audio patterns, and little testing has been done on scalability to longer patterns required for audio. A novel method of sound synthesis based on conceptors is introduced. Conceptular Synthesis is based on granular synthesis; sets of conceptors are trained to recall varying patterns from a single RNN, then a runtime mechanism switches between them, generating short patterns which are recombined into a longer sound. The quality of sound resynthesis using this technique is experimentally evaluated. Conceptor models are shown to resynthesise audio with a comparable quality to a close equivalent technique using echo state networks with stored patterns and output feedback. Conceptor models are also shown to excel in their malleability and potential for creative sound manipulation, in comparison to echo state network models which tend to fail when the same manipulations are applied. Examples are given demonstrating creative sonic possibilities, by exploiting conceptor pattern morphing, boolean conceptor logic and manipulation of RNN dynamics. Limitations of conceptor models are revealed with regards to reproduction quality, and pragmatic limitations are also shown, where rises in computation and memory requirements preclude the use of these models for training with longer sound samples. The techniques presented here represent an initial exploration of the sound synthesis potential of conceptors, demonstrating possible creative applications in sound design; future possibilities and research questions are outlined.

scholarly journals Sample-level sound synthesis with recurrent neural networks and conceptors

2018 ◽  
Author(s):  
Chris Kiefer
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Frequency Control ◽  
Synthesis Method ◽  
Sound Synthesis ◽  
Boolean Logic ◽  
Training Data ◽  
Reservoir Computing ◽  
Research Questions ◽  
Granular Synthesis

Conceptors are a recent development in the field of reservoir computing; they can be used to influence the dynamics of recurrent neural networks (RNNs), enabling generation of arbitrary patterns based on training data. Conceptors allow interpolation and extrapolation between patterns, and also provide a system of boolean logic for combining patterns together. Generation and manipulation of arbitrary patterns using conceptors has significant potential as a sound synthesis method for applications in computer music and procedural audio but has yet to be explored. Two novel methods of sound synthesis based on conceptors are introduced. Conceptular Synthesis is based on granular synthesis; sets of conceptors are trained to recall varying patterns from a single RNN, then a runtime mechanism switches between them, generating short patterns which are recombined into a longer sound. Conceptillators are trainable, pitch-controlled oscillators for harmonically rich waveforms, commonly used in a variety of sound synthesis applications. Both systems can exploit conceptor pattern morphing, boolean logic and manipulation of RNN dynamics, enabling new creative sonic possibilities. Experiments reveal how RNN runtime parameters can be used for pitch-independent timestretching and for precise frequency control of cyclic waveforms. They show how these techniques can create highly malleable sound synthesis models, trainable using short sound samples. Limitations are revealed with regards to reproduction quality, and pragmatic limitations are also shown, where exponential rises in computation and memory requirements preclude the use of these models for training with longer sound samples. The techniques presented here represent an initial exploration of the sound synthesis potential of conceptors; future possibilities and research questions are outlined, including possibilities in generative sound.

scholarly journals Sample-level sound synthesis with recurrent neural networks and conceptors

2018 ◽  
Author(s):  
Chris Kiefer
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Frequency Control ◽  
Synthesis Method ◽  
Sound Synthesis ◽  
Boolean Logic ◽  
Training Data ◽  
Reservoir Computing ◽  
Research Questions ◽  
Granular Synthesis

Conceptors are a recent development in the field of reservoir computing; they can be used to influence the dynamics of recurrent neural networks (RNNs), enabling generation of arbitrary patterns based on training data. Conceptors allow interpolation and extrapolation between patterns, and also provide a system of boolean logic for combining patterns together. Generation and manipulation of arbitrary patterns using conceptors has significant potential as a sound synthesis method for applications in computer music and procedural audio but has yet to be explored. Two novel methods of sound synthesis based on conceptors are introduced. Conceptular Synthesis is based on granular synthesis; sets of conceptors are trained to recall varying patterns from a single RNN, then a runtime mechanism switches between them, generating short patterns which are recombined into a longer sound. Conceptillators are trainable, pitch-controlled oscillators for harmonically rich waveforms, commonly used in a variety of sound synthesis applications. Both systems can exploit conceptor pattern morphing, boolean logic and manipulation of RNN dynamics, enabling new creative sonic possibilities. Experiments reveal how RNN runtime parameters can be used for pitch-independent timestretching and for precise frequency control of cyclic waveforms. They show how these techniques can create highly malleable sound synthesis models, trainable using short sound samples. Limitations are revealed with regards to reproduction quality, and pragmatic limitations are also shown, where exponential rises in computation and memory requirements preclude the use of these models for training with longer sound samples. The techniques presented here represent an initial exploration of the sound synthesis potential of conceptors; future possibilities and research questions are outlined, including possibilities in generative sound.

scholarly journals A Novel Method for Training an Echo State Network with Feedback-Error Learning

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Rikke Amilde Løvlid
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Training Data ◽  
Echo State Network ◽  
Training Method ◽  
Computationally Efficient ◽  
Echo State Networks ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning

Echo state networks are a relatively new type of recurrent neural networks that have shown great potentials for solving non-linear, temporal problems. The basic idea is to transform the low dimensional temporal input into a higher dimensional state, and then train the output connection weights to make the system output the target information. Because only the output weights are altered, training is typically quick and computationally efficient compared to training of other recurrent neural networks. This paper investigates using an echo state network to learn the inverse kinematics model of a robot simulator with feedback-error-learning. In this scheme teacher forcing is not perfect, and joint constraints on the simulator makes the feedback error inaccurate. A novel training method which is less influenced by the noise in the training data is proposed and compared to the traditional ESN training method.

scholarly journals Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation

2018 ◽  
Vol 8 (12) ◽  
pp. 2416 ◽  
Author(s):  
Ansi Zhang ◽  
Honglei Wang ◽  
Shaobo Li ◽  
Yuxin Cui ◽  
Zhonghao Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Recurrent Neural Networks ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Training Data ◽  
Good Prediction ◽  
Useful Life

Prognostics, such as remaining useful life (RUL) prediction, is a crucial task in condition-based maintenance. A major challenge in data-driven prognostics is the difficulty of obtaining a sufficient number of samples of failure progression. However, for traditional machine learning methods and deep neural networks, enough training data is a prerequisite to train good prediction models. In this work, we proposed a transfer learning algorithm based on Bi-directional Long Short-Term Memory (BLSTM) recurrent neural networks for RUL estimation, in which the models can be first trained on different but related datasets and then fine-tuned by the target dataset. Extensive experimental results show that transfer learning can in general improve the prediction models on the dataset with a small number of samples. There is one exception that when transferring from multi-type operating conditions to single operating conditions, transfer learning led to a worse result.

scholarly journals Clustered Echo State Networks for Signal Observation and Frequency Filtering

2020 ◽  
Author(s):  
Laércio Oliveira Junior ◽  
Florian Stelzer ◽  
Liang Zhao
Keyword(s):  
Dynamical System ◽  
Neural Networks ◽  
Input Signal ◽  
Recurrent Neural Networks ◽  
High Dimensional ◽  
Frequency Filtering ◽  
Echo State Networks ◽  
Chaotic Signals ◽  
Network Topologies ◽  
Dimensional Dynamical System

Echo State Networks (ESNs) are recurrent neural networks that map an input signal to a high-dimensional dynamical system, called reservoir, and possess adaptive output weights. The output weights are trained such that the ESN’s output signal fits the desired target signal. Classical reservoirs are sparse and randomly connected networks. In this article, we investigate the effect of different network topologies on the performance of ESNs. Specifically, we use two types of networks to construct clustered reservoirs of ESN: the clustered Erdös–Rényi and the clustered Barabási-Albert network model. Moreover, we compare the performance of these clustered ESNs (CESNs) and classical ESNs with the random reservoir by employing them to two different tasks: frequency filtering and the reconstruction of chaotic signals. By using a clustered topology, one can achieve a significant increase in the ESN’s performance.

scholarly journals Weather Nowcasting Using Deep Learning Techniques

2021 ◽  
Author(s):  
Makhamisa Senekane ◽  
Mhlambululi Mafu ◽  
Molibeli Benedict Taele
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Weather Forecasting ◽  
Network Models ◽  
Short Term ◽  
Neural Network Models ◽  
Precipitation Forecasting ◽  
Learning Techniques

Weather variations play a significant role in peoples’ short-term, medium-term or long-term planning. Therefore, understanding of weather patterns has become very important in decision making. Short-term weather forecasting (nowcasting) involves the prediction of weather over a short period of time; typically few hours. Different techniques have been proposed for short-term weather forecasting. Traditional techniques used for nowcasting are highly parametric, and hence complex. Recently, there has been a shift towards the use of artificial intelligence techniques for weather nowcasting. These include the use of machine learning techniques such as artificial neural networks. In this chapter, we report the use of deep learning techniques for weather nowcasting. Deep learning techniques were tested on meteorological data. Three deep learning techniques, namely multilayer perceptron, Elman recurrent neural networks and Jordan recurrent neural networks, were used in this work. Multilayer perceptron models achieved 91 and 75% accuracies for sunshine forecasting and precipitation forecasting respectively, Elman recurrent neural network models achieved accuracies of 96 and 97% for sunshine and precipitation forecasting respectively, while Jordan recurrent neural network models achieved accuracies of 97 and 97% for sunshine and precipitation nowcasting respectively. The results obtained underline the utility of using deep learning for weather nowcasting.

Partial AUC Optimisation Using Recurrent Neural Networks for Music Detection with Limited Training Data

2020 ◽  
Author(s):  
Pablo Gimeno ◽  
Victoria Mingote ◽  
Alfonso Ortega ◽  
Antonio Miguel ◽  
Eduardo Lleida
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Training Data ◽  
Partial Auc
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