A novel XML music information retrieval method using graph invariants

Pinto, Alberto A.; Haus, Goffredo

doi:10.1145/1281485.1281490

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…They are originally designed for the text mining task, which aim to discover the hidden topics in the text corpus to assist document clustering or classification. Due to their good extendibility and powerful representation, they have been successfully applied to many research areas, including analysis in image [12], video [10], genetics [5] and music [13]. Among these extensions, author topic models [18,20,17] were proposed to infer the hidden topics and author interests.…”

Section: Topic Modelsmentioning

confidence: 99%

Infinite Author Topic Model Based on Mixed Gamma-Negative Binomial Process

Xuan

Zhang

et al. 2015

2015 IEEE International Conference on Data Mining

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Incorporating the side information of text corpus, i.e., authors, time stamps, and emotional tags, into the traditional text mining models has gained significant interests in the area of information retrieval, statistical natural language processing, and machine learning. One branch of these works is the so-called Author Topic Model (ATM), which incorporates the authors's interests as side information into the classical topic model. However, the existing ATM needs to predefine the number of topics, which is difficult and inappropriate in many real-world settings. In this paper, we propose an Infinite Author Topic (IAT) model to resolve this issue. Instead of assigning a discrete probability on fixed number of topics, we use a stochastic process to determine the number of topics from the data itself. To be specific, we extend a gamma-negative binomial process to three levels in order to capture the author-document-keyword hierarchical structure. Furthermore, each document is assigned a mixed gamma process that accounts for the multi-author's contribution towards this document. An efficient Gibbs sampling inference algorithm with each conditional distribution being closed-form is developed for the IAT model. Experiments on several real-world datasets show the capabilities of our IAT model to learn the hidden topics, authors' interests on these topics and the number of topics simultaneously.

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Section: Topic Modelsmentioning

confidence: 99%

Infinite Author Topic Model Based on Mixed Gamma-Negative Binomial Process

Xuan

Zhang

et al. 2015

2015 IEEE International Conference on Data Mining

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“…In this perspective what the graph approach models is a global, time independent signature of the melody [9], [6]. In Figure 1 there is an example of three different instances of the same melodic structure obtained through a simple permutation of the pitch class set.…”

Section: Related Workmentioning

confidence: 99%

“…Secondly, a matching algorithm has to be designed that computes the similarity between objects such that the perceptual similarity between the original musical fragments is somehow reflected. A recent example of this representation/matching paradigm is the graph based representation [9] which exploits what has been defined as "interval connectivity" [10] and has proven to be an effective tool in melodic retrieval [10].…”

Section: Introductionmentioning

confidence: 99%

A generalized graph-spectral approach to melodic modeling and retrieval

Pinto

Tagliolato

2008

Proceedings of the 1st ACM International Conference on Multimedia Information Retrieval

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The development of both musicologically based and efficient music information retrieval metrics to query large music database is crucial in modern music information retrieval, knowledge management and database research. Graph spectral representation of pitch class sequences has proved to outperform other pitch class based melodic similarity methods.Here we compare different spectral approaches to structural queries in databases of symbolic music, which exploits mathematical music theory results to improve the descriptive power of representative graphs.In particular, we explore graph representation of other relevant music features like intervals. The experiments have been conducted on a subset of the RISM collection, and results have been evaluated against a ground truth for the same collection developed for the MIREX competition.

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“…Consequently, there has been increasing interest in the multimedia database and information systems communities [Lam and Tan 2001;Easley et al 2003;Pachet 2003;Pardo 2006;Pinto and Haus 2007] to study techniques for content-based music retrieval. Many techniques have recently been developed to support automatic classification or recognition of music based on instrument, genre, and other characteristics Liu and Huang 2002;Zhang 2003;Li and Ogihara 2004].…”

Section: Introductionmentioning

confidence: 99%

A novel framework for efficient automated singer identification in large music databases

Shen

Shepherd

Cui

et al. 2009

ACM Trans. Inf. Syst.

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Over the past decade, there has been explosive growth in the availability of multimedia data, particularly image, video, and music. Because of this, content-based music retrieval has attracted attention from the multimedia database and information retrieval communities. Content-based music retrieval requires us to be able to automatically identify particular characteristics of music data. One such characteristic, useful in a range of applications, is the identification of the singer in a musical piece. Unfortunately, existing approaches to this problem suffer from either low accuracy or poor scalability. In this article, we propose a novel scheme, called Hybrid Singer Identifier (HSI), for efficient automated singer recognition. HSI uses multiple low-level features extracted from both vocal and nonvocal music segments to enhance the identification process; it achieves this via a hybrid architecture that builds profiles of individual singer characteristics based on statistical mixture models. An extensive experimental study on a large music database demonstrates the superiority of our method over state-of-the-art approaches in terms of effectiveness, efficiency, scalability, and robustness.

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A novel XML music information retrieval method using graph invariants

Cited by 10 publications

References 12 publications

Infinite Author Topic Model Based on Mixed Gamma-Negative Binomial Process

Infinite Author Topic Model Based on Mixed Gamma-Negative Binomial Process

A generalized graph-spectral approach to melodic modeling and retrieval

A novel framework for efficient automated singer identification in large music databases

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