Polyphonic Music Information Retrieval Based on Multi-label Cascade Classification System

Jiang, Wenxin; Cohen, Amanda; Raś, Zbigniew W.

doi:10.1007/978-3-642-04141-9_6

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…Let S(d) = (X, A ∪ {d}, V ) be a decision system as introduced in [16,32], where X is a set of musical objects, A is the set of features used as classification attributes, d is a hierarchical decision attribute, V d is a set of values of the decision attribute, and V A is a set of values of all classification attributes. Figure 3 shows an example of a hierarchical decision attribute.…”

Section: Cascade Hierarchical Decision Systemsmentioning

confidence: 99%

“…As shown in Table 11, in Experiment 1 we applied the multiple label classification [16] based on features representing spectral flatness coefficients only. In Experiment 2 we used the power spectrum matching method, instead of features [15].…”

Section: Experiments and Evaluationmentioning

confidence: 99%

“…To our best knowledge, not much work has been done in investigating the applicability and usefulness of cascade classifiers in MIR area [16,22]. However, it is possible to construct a simple instrument family classifier with a low false recognition rate, which is called a classification pre-filter.…”

Section: Introductionmentioning

confidence: 99%

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Multi-label automatic indexing of music by cascade classifiers

Jiang

Raś

2013

Web Intelligence and Agent Systems: An International Journal

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Recently, numerous successful approaches have been developed for instrument recognition in monophonic sounds. Unfortunately, none of them can be successfully applied to polyphonic sounds. Identification of music instruments in polyphonic sounds is still difficult and challenging. This has stimulated a number of research projects on music sound separation, new features development, and more recently on hierarchically structured classifiers used in content-based music recommender systems. This paper introduces a hierarchically structured cascade classification system to estimate multiple timbre information from the polyphonic sound by classification which is based on acoustic features and short-term power spectrum matching. This cascade system makes a first estimate on the higher level decision attribute which stands for the musical instrument family. Then, the further estimation is done within that specific family range. Experiments showed better performance of a hierarchical system than the traditional flat classification method which directly estimates the instrument without higher level of family information analysis. Traditional hierarchical structures were constructed in human semantics, which are meaningful from human perspective but not appropriate for a cascade system. We introduce a new hierarchical instrument schema according to the clustering results of the acoustic features. This new schema better describes the similarity among different instruments or among different playing techniques of the same instrument. The classification results show the higher accuracy of cascade system with the new schema compared to the traditional schemas.

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Section: Cascade Hierarchical Decision Systemsmentioning

confidence: 99%

Section: Experiments and Evaluationmentioning

confidence: 99%

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Multi-label automatic indexing of music by cascade classifiers

Jiang

Raś

2013

Web Intelligence and Agent Systems: An International Journal

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“…Next, automatic recognition of instruments in audio data was performed on polyphonic polytimbral data, see e.g. [3], [12], [13], [14], [19], [30], [32], [35], also including investigations on separation of the sounds from the audio sources (see e.g. [8]).…”

Section: Automatic Identification Of Musical Instruments In Sound Recmentioning

confidence: 99%

Recognition of Instrument Timbres in Real Polytimbral Audio Recordings

Kubera

Wieczorkowska

Raś

et al. 2010

Machine Learning and Knowledge Discovery in Databases

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Automatic recognition of multiple musical instruments in polyphonic and polytimbral music is a difficult task, but often attempted to perform by MIR researchers recently. In papers published so far, the proposed systems were validated mainly on audio data obtained through mixing of isolated sounds of musical instruments. This paper tests recognition of instruments in real recordings, using a recognition system which has multilabel and hierarchical structure. Random forest classifiers were applied to build the system. Evaluation of our model was performed on audio recordings of classical music. The obtained results are shown and discussed in the paper.

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Music Information Retrieval with Temporal Features and Timbre

Tzacheva

Bell

2010

Active Media Technology

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Polyphonic Music Information Retrieval Based on Multi-label Cascade Classification System

Cited by 5 publications

References 21 publications

Multi-label automatic indexing of music by cascade classifiers

Multi-label automatic indexing of music by cascade classifiers

Recognition of Instrument Timbres in Real Polytimbral Audio Recordings

Music Information Retrieval with Temporal Features and Timbre

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