Music Audio Signal Prediction using Machine Learning

Typ
Examensarbete för masterexamen
Program
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
Citation
Arkitekt (konstruktör)
Geografisk plats
Byggnad (typ)
Byggår
Modelltyp
Skala
Teknik / material