Time Series Models for Semantic Music Annotation

被引：30

作者：

Coviello, Emanuele ^{[1
]}

Chan, Antoni B. ^{[2
]}

Lanckriet, Gert ^{[1
]}

机构：

[1] Univ Calif San Diego, Dept Elect & Comp Engn, La Jolla, CA 92093 USA

[2] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China

来源：

IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING | 2011年 / 19卷 / 05期

基金：

美国国家科学基金会;

关键词：

Audio annotation and retrieval; dynamic texture model; music information retrieval; CLASSIFICATION;

D O I：

10.1109/TASL.2010.2090148

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Many state-of-the-art systems for automatic music tagging model music based on bag-of-features representations which give little or no account of temporal dynamics, a key characteristic of the audio signal. We describe a novel approach to automatic music annotation and retrieval that captures temporal (e. g., rhythmical) aspects as well as timbral content. The proposed approach leverages a recently proposed song model that is based on a generative time series model of the musical content-the dynamic texture mixture (DTM) model-that treats fragments of audio as the output of a linear dynamical system. To model characteristic temporal dynamics and timbral content at the tag level, a novel, efficient, and hierarchical expectation-maximization (EM) algorithm for DTM (HEM-DTM) is used to summarize the common information shared by DTMs modeling individual songs associated with a tag. Experiments show learning the semantics of music benefits from modeling temporal dynamics.

引用

页码：1343 / 1359

页数：17