Music Audio Signal Prediction using Machine Learning
Typ
Examensarbete för masterexamen
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2022
Författare
Gentile, Ivan
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Even though considerable advancements have been made in time series
forecasting for audio, there are still many unexplored aspects. An objective of
the analysis is to develop a viable product to replace the look-ahead functions of
audio dynamic range compressors. Towards this end, and given the suitability
of neural networks for predictive purposes, this project discusses the application
of MultyLayer Perceptrons (MLPs) and Long-Short Term Memory (LSTMs) for
addressing this research question. The numerical experiments focuses on the
predictions of this systems. It is analyzed how changing window length (number
of inputs), prediction steps (number of outputs), and sampling frequency (dataset
resolution) affects prediction quality. The findings indicate that, after a threshold,
increasing number of inputs yields diminishing rewards.
Beskrivning
Ämne/nyckelord
Audio Digital Signal Processing , Machine Learning , Artificial Intelligence , Time Series Forecasting , Audio Machine Learning Application , Signal Prediction , Time Samples Prediction