2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2013
DOI: 10.1109/mlsp.2013.6661957
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Acoustic scene analysis based on latent acoustic topic and event allocation

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Cited by 18 publications
(10 citation statements)
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“…The self-organizing map (SOM) can be latent analysis, and SOMbased music clustering has been proposed [39]. Futhermore, there exist many research papers on acoustic analysis based on topic modeling (see, for example [40][41][42][43]). There are, however, none that dealt with singing features.…”
Section: Methodsmentioning
confidence: 99%
“…The self-organizing map (SOM) can be latent analysis, and SOMbased music clustering has been proposed [39]. Futhermore, there exist many research papers on acoustic analysis based on topic modeling (see, for example [40][41][42][43]). There are, however, none that dealt with singing features.…”
Section: Methodsmentioning
confidence: 99%
“…Acoustic scene estimation methods that focus on the latent structure of an acoustic scene lying in acoustic event sequences have also been proposed, and these are called the "acoustic topic model (ATM)" [11]- [13]. In the ATM, acoustic scenes are represented as a distribution of latent variables called "acoustic topics," and a generative process of acoustic event sequences e s is modeled not by using acoustic scenes directly but through distributions of acoustic topics as shown in Fig.…”
Section: Acoustic Topic Modelmentioning
confidence: 99%
“…We call this model the "acoustic scene model (ASM)." Kim et al [11], Lee et al [12], and Imoto et al [13], [14] proposed another unsupervised generative model for analyzing acoustic scenes on the basis of the "acoustic topic model (ATM)." The ATM is an unsupervised generative model that generates acoustic event sequences from acoustic scenes through the latent structure of the acoustic scene lying in a combination of acoustic events, which is called the "acoustic topic."…”
Section: Introductionmentioning
confidence: 99%
“…In this paper we introduce AT into audio classification based on the idea that an audio document can be expressed as a combination of acoustic topics as well as a combination of acoustic events. A similar idea is proposed in [15], where a LATEA (Latent Acoustic Topic and Event Allocation) model was proposed for acoustic scene analyzing. The difference is that instead of expressing an audio document as a combination of acoustic events, LATEA expresses an acoustic topic as a combination of acoustic events.…”
Section: Related Workmentioning
confidence: 99%