Analysis of Structural Complexity Features for Music Genre Recognition

Ginsel, Philipp; Vatolkin, Igor; Rudolph, G.

doi:10.1109/cec48606.2020.9185540

Cited by 7 publications

(6 citation statements)

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“…The Seyerlehner:1517-Artists dataset contains 3,180 original tracks from 23 music genres. From the original tracks, only tracks from different artists in each category were selected in [1]. MagnaTagAtune comprises 4,476 songs belonging to 24 musical genres, making it the most diverse collection [29].…”

Section: Related Workmentioning

confidence: 99%

“…Closely related to the DT, the random forest (RF) is an ensemble learning method that randomly selects acoustic features from several different sets [1], [2], [11]. The k-nearest neighbor (kNN) classifier calculates the distance between the test music pieces and training music pieces in terms of the acoustic features [1], [8], [9]. The naïve Bayes (NB) classifier uses Bayesian theory for deciding the relevant tag of music pieces [2], [5], [20].…”

Section: Related Workmentioning

confidence: 99%

“…The two popular frameworks are Music Analysis, Retrieval and Synthesis for Audio Signals (MARSYAS) [2], [6], [24]- [26] and Advanced MUSic Explorer (AMUSE) [5], [20], [21]. Feature extractors such as jAudio [18], [29], MIRtoolbox [4], [12], [18], and the Chordino Vamp Plugin [1] can be used both independently or based on the aforementioned two frameworks. VOICEBOX [17], MPEG-7 [18], OpenSMILE [9], CLAM [19], [23], and librosa from Python [3] are also used for feature extraction.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, the One-Against-All (OAA) and Round Robin (RR) space decomposition approaches have been combined with GA [6], [24], [25]. S-Metric Selection Evolutionary Multiobjective Algorithms (SMS-EMOA) [1], [5], [20], [21] are often selected as an optimization heuristic in MTC studies. They sort the population into several solution fronts through fast non-dominated sorting [20].…”

Section: Related Workmentioning

confidence: 99%

“…A UTOMATIC music tag classification (MTC) is used to find a relevant music tag, such as emotion or genre, for a music excerpt based on its music signal or extracted acoustic features [1]- [3]. This task can be achieved by training a classifier using music excerpts with relevant tags annotated by a human being.…”

Section: Introductionmentioning

confidence: 99%

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Toward a Fair Evaluation and Analysis of Feature Selection for Music Tag Classification

et al. 2021

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Accurate music tag classification of music clips has been attracting great attention recently, because it allows one to provide various music excerpts, including unpopular ones, to users based on the clips' acoustic similarities. Given a user's preferred music, acoustic features are extracted and then fed into the classifier, which outputs the related tag to recommend new music. Furthermore, the accuracy of the tag classifiers can be improved by selecting the best feature subset based on the domain to which the tag belongs. However, recent studies have struggled to evaluate the superiority of various classifiers because they utilize different feature extractors. In this study, to conduct a direct comparison of existing methods of classification, we create 20 music datasets with the same acoustic feature structure. In addition, we propose an effective evolutionary feature selection algorithm to evaluate the effectiveness of feature selection for tag classification. Our experiments demonstrate that the proposed method improves the accuracy of tag classification, and the analysis with multiple datasets provides valuable insights, such as the important features for general music tag classification in target domains.INDEX TERMS Music tag classification, feature selection, machine learning, evolutionary algorithm.

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Section: Related Workmentioning

confidence: 99%