Advanced

A Parametric Method for Multi-Pitch Estimation

Elvander, Filip (2015) FMS820 20151
Mathematical Statistics
Abstract
This thesis proposes a novel method for multi-pitch estimation. The method
operates by posing pitch estimation as a sparse recovery problem which is solved
using convex optimization techniques. In that respect, it is an extension of an
earlier presented estimation method based on the group-LASSO. However, by
introducing an adaptive total variation penalty, the proposed method requires
fewer user supplied parameters, thereby simplifying the estimation procedure.
The method is shown to have comparable to superior performance in low noise
environments when compared to three standard multi-pitch estimation methods
as well as the predecessor method. Also presented is a scheme for automatic
selection of the regularization parameters,... (More)
This thesis proposes a novel method for multi-pitch estimation. The method
operates by posing pitch estimation as a sparse recovery problem which is solved
using convex optimization techniques. In that respect, it is an extension of an
earlier presented estimation method based on the group-LASSO. However, by
introducing an adaptive total variation penalty, the proposed method requires
fewer user supplied parameters, thereby simplifying the estimation procedure.
The method is shown to have comparable to superior performance in low noise
environments when compared to three standard multi-pitch estimation methods
as well as the predecessor method. Also presented is a scheme for automatic
selection of the regularization parameters, thereby making the method more user
friendly. Used together with this scheme, the proposed method is shown to yield
accurate, although not statistically efficent, pitch Estimates when evaluated on synthetic speech data. (Less)
Please use this url to cite or link to this publication:
author
Elvander, Filip
supervisor
organization
course
FMS820 20151
year
type
H2 - Master's Degree (Two Years)
subject
language
English
id
5470871
date added to LUP
2015-06-10 11:24:03
date last changed
2015-06-10 11:47:41
@misc{5470871,
  abstract     = {This thesis proposes a novel method for multi-pitch estimation. The method
operates by posing pitch estimation as a sparse recovery problem which is solved
using convex optimization techniques. In that respect, it is an extension of an
earlier presented estimation method based on the group-LASSO. However, by
introducing an adaptive total variation penalty, the proposed method requires
fewer user supplied parameters, thereby simplifying the estimation procedure.
The method is shown to have comparable to superior performance in low noise
environments when compared to three standard multi-pitch estimation methods
as well as the predecessor method. Also presented is a scheme for automatic
selection of the regularization parameters, thereby making the method more user
friendly. Used together with this scheme, the proposed method is shown to yield
accurate, although not statistically efficent, pitch Estimates when evaluated on synthetic speech data.},
  author       = {Elvander, Filip},
  language     = {eng},
  note         = {Student Paper},
  title        = {A Parametric Method for Multi-Pitch Estimation},
  year         = {2015},
}