scholarly journals LSTM Based Music Generation System

IARJSET ◽  
2019 ◽  
Vol 6 (5) ◽  
pp. 47-54
Author(s):  
Sanidhya Mangal ◽  
Rahul Modak ◽  
Poorva Joshi
Keyword(s):  
Generation System ◽  
Music Generation

scholarly journals Review on Automatic Music Generation System with Deep learning

2021 ◽  
pp. 332-335
Author(s):  
Prof. Amita Suke ◽  
Prof. Khemutai Tighare ◽  
Yogeshwari Kamble
Keyword(s):  
Deep Learning ◽  
Generation System ◽  
Easy Task ◽  
Present Writing ◽  
Long Time ◽  
Music Generation ◽  
Human Skills ◽  
Automatic Music Generation ◽  
Music Lyrics

The music lyrics that we generally listen are human written and no machine involvement is present. Writing music has never been easy task, lot of challenges are involved to write because the music lyrics need to be meaningful and at the same time it needs to be in harmony and synchronised with the music being play over it. They are written by experienced artist who have been writing music lyrics form long time. This project tries to automate music lyrics generation using computerized program and deep learning which we produce lyrics and reduce the load on human skills and may generate new lyrics and a really faster rate than humans ever can. This project will generate the music with the assistance of human and AI

scholarly journals Chaotic Music Generation System Using Music Conductor Gesture

2013 ◽  
Vol 17 (2) ◽  
pp. 194-200 ◽  
Author(s):  
Shuai Chen ◽  
◽  
Yoichiro Maeda ◽  
Yasutake Takahashi
Keyword(s):  
Real Time ◽  
Microsoft Kinect ◽  
Human Beings ◽  
Generation System ◽  
Interactive Music ◽  
Music Generation

In research on interactive music generation, we propose a music generation method in which the computer generates music under the recognition of a humanmusic conductor’s gestures. In this research, generated music is tuned by parameters of a network of chaotic elements which are determined by the recognized gesture in real time. The music conductor’s hand motions are detected by Microsoft Kinect in this system. Music theories are embedded in the algorithm and, as a result, generated music is richer. Furthermore, we constructed the music generation system and performed experiments for generating music composed by human beings.

scholarly journals Pattern-Based Music Generation with Wasserstein Autoencoders and PRC Descriptions

2020 ◽  
Author(s):  
Valentijn Borghuis ◽  
Luca Angioloni ◽  
Lorenzo Brusci ◽  
Paolo Frasconi
Keyword(s):  
Generation System ◽  
Latent Space ◽  
Novel Variant ◽  
Music Generation

We demonstrate a pattern-based MIDI music generation system with a generation strategy based on Wasserstein autoencoders and a novel variant of pianoroll descriptions of patterns which employs separate channels for note velocities and note durations and can be fed into classic DCGAN-style convolutional architectures. We trained the system on two new datasets (in the acid-jazz and high-pop genres) composed by musicians in our team with music generation in mind. Our demonstration shows that moving smoothly in the latent space allows us to generate meaningful sequences of four-bars patterns.

scholarly journals Lyrics to Music Generator: Statistical Approach

2021 ◽  
Author(s):  
V.N Aditya Datta Chivukula ◽  
Abhiram Reddy Cholleti ◽  
Rakesh Chandra Balabantaray
Keyword(s):  
Language Processing ◽  
Statistical Approach ◽  
Statistical Machine Learning ◽  
Audio Signals ◽  
Generation System ◽  
Machine Learning Methods ◽  
Short Period ◽  
Recent Developments ◽  
Music Generation ◽  
Music Signals

Natural Language Processing is in growing demand with recent developments. This Generator model is one such example of a music generation system conditioned on lyrics. The model proposed has been tested on songs having lyrics written only in English, but the idea can be generalized to various languages. This paper’s objective is to mainly explain how one can create a music generator using statistical machine learning methods. This paper also explains how effectively outputs can be formulated, which are the music signals as they are million sized over a short period frame. The parameters mentioned in the paper only serve an explanatory purpose. This paper discusses the effective statistical formulation of output thereby decreasing the vast amount of estimation of output parameters, and how to reconstruct the audio signals from predicted parameters by using ‘phase-shift algorithm’.

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