Paper Key : IRJ************612
Author: Uttara Bahad,Rishika Sardana,Sakshi Sanjay Chalke,Krutika Prakash Bhagane ,Ujwala Gaikwad
Date Published: 06 Oct 2023
Abstract
The thing that is common to all the people across the world, or the thing that transforms boundaries and connects us all is music. According to statistics every human hears approximately 15 minutes of music every day. Music is one category that all humans consume in all their moods, no matter if they are feeling happy, sad, angry, or lonely. Hence to cater to this need of music generating industry is a multi-billion industry. Everyday millions of songsmusic all around the world is being produced and a variety of people like directors, producers, composers, singers, music-writers are included in this lengthy process.Through our research we aim to produce a mock model which will make music creating a less time consuming, lengthy, and costly process. This can be done by using AutoEncoders which are a part of unsupervised neural networks. This machine learning technique will help us generate music, which follows a specific pattern of chords.We aim to create a realistic music tone which is a synthetic data. This can be done by training VAE with Keras and by performing Short-TimeInverse Short-Time Fourier Transformations.
DOI LINK : 10.56726/IRJMETS45083 https://www.doi.org/10.56726/IRJMETS45083