Work

Learning Musical Pitch Structures With Hierarchical Hidden Markov Models

Year: 2005

Type: article

Abstract: In this paper we attempt to demonstrate the strengths of Hierarchical Hidden Markov Models (HHMMs) in the representation and modelling of musical structures. We show how relatively simple HHMMs, containing a minimum of expert knowledge, use their advantage of having multiple layers to perform well on tasks where flat Hidden Markov Models (HMMs) struggle. The examples in this paper show a HHMM's performance at extracting higherlevel musical properties through the construction of simple pitch sequences, correctly representing the data set on which it was trained.

Source: Edinburgh Research Explorer (University of Edinburgh)

Authors: Weiland, Michèle, Smaill, Alan, Nelson, Peter

Institution: University of Edinburgh

Language: English

Topic: Music and Audio Processing

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2616071181

FWCI:

Cites: 6

Cited by: 16

Related to: 10