We propose a framework for computer music composition that uses resilient propagation (RProp) and long short term memory (LSTM) recurrent neural network. In this paper, we show that LSTM network learns the structure and characteristics of music pieces properly by demonstrating its ability to recreate music. We also show that predicting existing music using RProp outperforms Back propagation thr...

A two-layer Recurrent Neural Network Model (RNNM) and an improved Backpropagation-through-time method of its learning are described. For a complex nonlinear plants identification, a fuzzy-neural multi-model, is proposed. The proposed fuzzy-neural model, containing two RNNMs is applied for real-time identification of nonlinear mechanical system. The simulation and experimental results confirm th...

Let m be the number of time-varying variables for storing temporal events in a fully recurrent sequence processing network. Let R time be the ratio between the number of operations per time step (for an exact gradient based supervised sequence learning algorithm), and m. Let R space be the ratio between the maximum number of storage cells necessary for learning arbitrary sequences, and m. With ...

The vast majority of YouTube videos never become popular, languishing in obscurity with few views, no likes, and no comments. We use information theoretical measures based on entropy to examine how time series distributions of common measures of popularity in videos from YouTube’s “Trending videos” and “Most recent” video feeds relate to the theoretical concept of attention. While most of the v...

Musical strings play important roles in the timbre of a stringed instrument. An instrument installed with different strings can produce very different sounds. To model a plucked-string instrument physically, analyzing its strings may be a good starting point. In this paper, a novel strategy for the modeling and analysis of acoustic musical strings is proposed. In order to obtain the vibration o...

As mentioned in Section 4 of main paper, the intricate relational and temporal dependencies between data points in our setting limits our ability to efficiently train by decomposing events into independent sequences. To address this challenge, we design an efficient Global BPTT algorithm presented below. During each step of training, we build computational graph using consecutive events in the ...

A major drawback of backpropagation through time (BPTT) is the difficulty of learning long-term dependencies, coming from having to propagate credit information backwards through every single step of the forward computation. This makes BPTT both computationally impractical and biologically implausible. For this reason, full backpropagation through time is rarely used on long sequences, and trun...

The suitability of hemp fibres as green support material for immobilization of Acidithiobacillus ferrooxidans cells in ferrous iron bio-oxidation process was investigated. A series of experiments were carried out in shake flasks and bioreactor for both cell immobilization and iron bio-oxidation. In flask experiments, five successive batch colonization cycles resulted in 6-fold increase...