Sinusoidal modeling is one of the most common techniques for general purpose audio synthesis and analysis. Owing to the ever increasing amount of available computational resources, nowadays practically all types of sounds can be constructed up to a certain degree of perceptual accuracy. However, the method is computationally expensive and can for some cases, particularly for transient signals, still exceed the available computational resources. In this work methods derived from the realm of machine learning are exploited to provide a simple and efficient means to estimate the achievable reconstruction quality. The peculiarities of common classes of musical instruments are discussed and finally, the existing metrics are extended by information on the signal's phase propagation to allow for more accurate estimations.

Authors: Hollomey, Clara; Moore, David; Knox, Don; Brimijoin, W. Owen; Whitmer, William
Affiliations: Glasgow Caledonian University, Glasgow, Scotland, UK; MRC/CSO Institute of Hearing Research, Glasgow, Scotland, UK(See document for exact affiliation information.)
AES Convention: 140 (May 2016) Paper Number: 9492
Publication Date: May 26, 2016 Import into BibTeX
Subject: Audio Signal Processing: Coding, Encoding, and Perception
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=18191

Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!

This paper costs $33 for non-members and is free for AES members and E-Library subscribers.

Learn more about the AES E-Library

E-Library Location: (CD 140Papers)   /conv/140/9492.pdf

Start a discussion about this paper!