&lt;title&gt;Semi-automatic approach for music classification&lt;/title&gt;

Zhang, Tong

doi:10.1117/12.511787

Cited by 10 publications

(12 citation statements)

References 7 publications

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“…Some of the works that have used class hierarchies in this application domain are: (Burred and Lerch 2003;McKay and Fujinaga 2004;Li and Ogihara 2005;Brecheisen et al 2006a; Barbedo and Lopes 2007;DeCoro et al 2007;Silla Jr and Freitas 2009b). The idea of using the hierarchy for browsing and retrieval has been explored so far in two existing tools for organizing music collections: Zhang (2003) demonstrates an end-user system based on the use of hierarchies to organize music collections; and Brecheisen et al (2006b) allows the system to have user feed-back in order to re-organize the pre-existing class hierarchy as the users see fit. Figure 10 illustrates the audio class hierarchy used in Burred and Lerch (2003).…”

Section: Protein Function Predictionmentioning

confidence: 98%

A survey of hierarchical classification across different application domains

Silla

Freitas

2010

Data Min Knowl Disc

831

539

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In this survey we discuss the task of hierarchical classification. The literature about this field is scattered across very different application domains and for that reason research in one domain is often done unaware of methods developed in other domains. We define what is the task of hierarchical classification and discuss why some related tasks should not be considered hierarchical classification. We also present a new perspective about some existing hierarchical classification approaches, and based on that perspective we propose a new unifying framework to classify the existing approaches. We also present a review of empirical comparisons of the existing methods reported in the literature as well as a conceptual comparison of those methods at a high level of abstraction, discussing their advantages and disadvantages.

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Section: Protein Function Predictionmentioning

confidence: 98%

A survey of hierarchical classification across different application domains

Silla

Freitas

2010

Data Min Knowl Disc

831

539

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“…Therefore, we propose an extension of the detected patterns by using the spectral peak track feature, allowing to capture such small pitch variations. Recently, a similar proposal has been suggested in [4]. However, such a procedure has not been implemented.…”

Section: Spectral Peak Track: Feature Extraction and Pattern Classifimentioning

confidence: 99%

“…This segmentation helps to organize the music library, allowing the immediate access to the segments that contain singing voice. Moreover, it provides support for some important applications, such as: i) automatic transcription of lyrics, as suggested in [2]; ii) separation between the singing voice and the musical instruments within a music signal; iii) automatic singer identification [3]; iv) complete classification of a musical piece, as suggested in [4]. Singing voice segments can be classified as vocal solo or chorus.…”

Section: Introductionmentioning

confidence: 99%

<title>Segmentation of singing voice within music signals</title>

2004

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This paper proposes a novel approach to accomplish the automatic segmentation of singing voice within music signals, based on the difference between the dynamic harmonic content of singing voice and that of musical instrument signals. The obtained results are compared with those of another approach proposed in the literature, considering the same music database. For both techniques, an accuracy rate around 80% is obtained, even using a more rigorous performance measure for our approach only. As an advantage, the new procedure presents lower computational complexity. In addition, we discuss other results obtained by extending the tests over the whole database (upholding the same performance level) and by discriminating the error types (boundaries shifted in time, insertion and deletion of singing segments). The analysis of these errors suggests some alternative ways of reducing them, as for example, to adopt a confidence level based on a minimum harmonic content for the input signals. In this way, considering only signals with confidence level equal to one, the obtained performance is improved to almost 87%.

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“…In [3], music pieces are classified into either rock or classic using k-NN and MLP classifiers. Zhang [4] proposes a method for a hierarchical genre classification which follows a fixed schema and where is only limited support for user-created genre folders. Moreover, the above mentioned hierarchical classification methods do not take full advantage of MI and MR music objects.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Genre Classification for Large Music Collections

Brecheisen¹,

Kriegel²,

Kunath³

et al. 2006

2006 IEEE International Conference on Multimedia and Expo

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The rapid progress in digital music distribution has lead to the creation of large collections of music. There is a need for content-based music classification methods to organize these collections automatically using a given genre taxonomy. To provide a versatile description of the music content, several kinds of features like rhythm, pitch or timbre characteristics are commonly used. Taking the highly dynamic nature of music into account, each of these features should be calculated up to several hundreds of times per second. Thus, a piece of music is represented by a complex object given by several large sets of feature vectors. In this paper, we propose a novel approach for the hierarchical classification of music pieces into a genre taxonomy. Our approach is able to handle multiple characteristics of music content and achieves a high classification accuracy efficiently, as shown in our experiments performed on a real world data set.

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<title>Semi-automatic approach for music classification</title>

Cited by 10 publications

References 7 publications

A survey of hierarchical classification across different application domains

A survey of hierarchical classification across different application domains

<title>Segmentation of singing voice within music signals</title>

Hierarchical Genre Classification for Large Music Collections

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