Paper Info
Title
Intra-note segmentation via sticky HMM with DP emission
Abstract
This paper presents an intra-note segmentation method for mono-phonic recordings based on acoustic feature variation; each musical note is separated into onset, steady and offset states. The task of intra-note segmentation from audio signals is detecting change points of acoustic feature. In proposed method, the Markov process is assumed on state transition, and time-varying acoustic feature is represented by three Dirichlet processes (DP) that are emitted by the each state. In order to express the generative process, the sticky hidden Markov model (HMM) with DP emission is employed. This modeling allows us to automatically estimate the state transition while avoiding the model selection problem by assuming countably infinite of possible acoustic feature in musical notes. Experimental result shows that the detection accuracy of onset-to-steady and steady-to-offset were improved 2.3 points and 20.7 points from previous method, respectively.
Year
DOI
Venue
2014
10.1109/ICASSP.2014.6853978
Acoustics, Speech and Signal Processing
Keywords
Field
DocType
audio signal processing,hidden Markov models,music,musical instruments,DP emission,Dirichlet processes,Markov process,acoustic feature variation,audio signals,hidden Markov model,intranote segmentation,model selection problem,monophonic recordings,sticky HMM,Dirichlet process,hidden Markov model,intra-note segmentation,music information retrieval
Audio signal,Markov process,Pattern recognition,Segmentation,Computer science,Markov model,Model selection,Artificial intelligence,Dirichlet distribution,Musical note,Hidden Markov model
Conference
ISSN
Citations 
PageRank 
1520-6149
0
0.34
References 
Authors
8
2
Name
Order
Citations
PageRank
Koizumi Yuma14111.75
Katunobu Itou231944.36