Music Generation Using Deep Learning Techniques
01 Jan 2019-pp 327-335
TL;DR: RBM and Recurrent Neural Network Restricted Boltzmann Machine are used for music generation by training it on a collection of Musical Instrument Digital Interface (MIDI) files.
Abstract: Deep learning techniques for generating music that has melody and harmony and is similar to music compositions by human beings is something that has fascinated researchers in the field of artificial intelligence. Nowadays, deep learning is being used for solving various problems in numerous artistic fields. There has been a new trend of using deep learning models for various applications in the field of music that has attracted much attention, and automated music generation has been an active area of research that lies in the cross section of artificial intelligence and audio synthesis. Previously, the work in automated music generation was solely focused on generating music, which consisted of a single melody, which is also known as monophonic music. More recently, research work related to the automated generation of polyphonic music, music, which consists of multiple melodies, has met partial success with the help of estimation of time series probability density. In this paper, we use Restricted Boltzmann Machine (RBM) and Recurrent Neural Network Restricted Boltzmann Machine for music generation by training it on a collection of Musical Instrument Digital Interface (MIDI) files.
TL;DR: Various communication protocols, namely Zigbee, Bluetooth, Near Field Communication (NFC), LoRA, etc. are presented, and the difference between different communication protocols is provided.
Abstract: Internet of Things (IoT) consists of sensors embed with physical objects that are connected to the Internet and able to establish the communication between them without human intervene applications are industry, transportation, healthcare, robotics, smart agriculture, etc. The communication technology plays a crucial role in IoT to transfer the data from one place to another place through Internet. This paper presents various communication protocols, namely Zigbee, Bluetooth, Near Field Communication (NFC), LoRA, etc. Later, it provides the difference between different communication protocols. Finally, the overall discussion about the communication protocols in IoT.
••04 Oct 2018
TL;DR: This paper presents a proof of concept and builds a recurrent neural network architecture capable of generalizing appropriate musical raw audio tracks and addressing the problem of automated music synthesis using deep neural networks.
Abstract: In this paper, we address the problem of automated music synthesis using deep neural networks and ask whether neural networks are capable of realizing timing, pitch accuracy and pattern generalization for automated music generation when processing raw audio data. To this end, we present a proof of concept and build a recurrent neural network architecture capable of generalizing appropriate musical raw audio tracks.
Cites methods from "Music Generation Using Deep Learnin..."
...A simple approach is to perform regression in the frequency domain using RNNs and to use a seed sequence after training to generate novel sequences [14,9]....
••02 Nov 2016
TL;DR: TensorFlow as mentioned in this paper is a machine learning system that operates at large scale and in heterogeneous environments, using dataflow graphs to represent computation, shared state, and the operations that mutate that state.
Abstract: TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Tensor-Flow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous "parameter server" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that TensorFlow achieves for several real-world applications.
•08 Dec 2008
TL;DR: The Recurrent TRBM is introduced, which is a very slight modification of the TRBM for which exact inference is very easy and exact gradient learning is almost tractable.
Abstract: The Temporal Restricted Boltzmann Machine (TRBM) is a probabilistic model for sequences that is able to successfully model (ie, generate nice-looking samples of) several very high dimensional sequences, such as motion capture data and the pixels of low resolution videos of balls bouncing in a box The major disadvantage of the TRBM is that exact inference is extremely hard, since even computing a Gibbs update for a single variable of the posterior is exponentially expensive This difficulty has necessitated the use of a heuristic inference procedure, that nonetheless was accurate enough for successful learning In this paper we introduce the Recurrent TRBM, which is a very slight modification of the TRBM for which exact inference is very easy and exact gradient learning is almost tractable We demonstrate that the RTRBM is better than an analogous TRBM at generating motion capture and videos of bouncing balls
TL;DR: This article presents the mathematical framework for the development of Wave-Nets and discusses the various aspects of their practical implementation and presents two examples on the application; the prediction of a chaotic time-series, representing population dynamics, and the classification of experimental data for process fault diagnosis.
Abstract: A Wave-Net is an artificial neural network with one hidden layer of nodes, whose basis functions are drawn from a family of orthonormal wavelets. The good localization characteristics of the basis functions, both in the input and frequency domains, allow hierarchical, multiresolution learning of input-output maps from experimental data. Furthermore, Wave-Nets allow explicit estimation for global and local prediction error-bounds, and thus lend themselves to a rigorous and explicit design of the network. This article presents the mathematical framework for the development of Wave-Nets and discusses the various aspects of their practical implementation. Computational complexity arguments prove that the training and adaptation efficiency of Wave-Nets is at least an order of magnitude better than other networks. In addition, it presents two examples on the application of Wave-Nets; (a) the prediction of a chaotic time-series, representing population dynamics, and (b) the classification of experimental data for process fault diagnosis.
••20 Jun 2003
TL;DR: The authors introduce a continuous stochastic generative model that can model continuous data, with a simple and reliable training algorithm, that is computationally inexpensive in both software and hardware.
Abstract: The authors introduce a continuous stochastic generative model that can model continuous data, with a simple and reliable training algorithm. The architecture is a continuous restricted Boltzmann machine, with one step of Gibbs sampling, to minimise contrastive divergence, replacing a time-consuming relaxation search. With a small approximation, the training algorithm requires only addition and multiplication and is thus computationally inexpensive in both software and hardware. The capabilities of the model are demonstrated and explored with both artificial and real data.
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