A survey on symbolic data-based music genre classification

Corrêa, Débora C.; Rodrigues, Francisco Ap.

doi:10.1016/j.eswa.2016.04.008

Cited by 66 publications

(39 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Referring to the review summary by article [3], the proposed TSMGC approach belongs to a machine learning enabled symbolic data-based MGC that uses global-based features. But the TSMGC can enhance the performance of MGC by adding the following points: (1) combines various musical features extracted from the RMH and the calculated entropy, (2) applies the weight of features and their AHP-determined impact values on the basis of probability density function, (3) performs a machine learning based two-step classification process to more accurately categorize a music into a main-class and further sub-classes.…”

Section: Discussionmentioning

confidence: 99%

“…Corrêa and Rodrigues [3] presented a review of the most important studies on MGC. In an effort to compare the pros and cons of the published research literature, they investigated the most common music features and classification techniques used.…”

Section: Introductionmentioning

confidence: 99%

“…However in practice, most studies adopt music features based on music content to perform MGC, in particular for automatic MGC in a systematic manner [10,11]. Although both audio content-based and symbolic content-based representations have distinct advantages and drawbacks, the MGC approaches that rely on symbolic content-based features deliver better accuracy than those that rely on audio content-based features, because the symbolic data can offer higher-level music representation [3]. In addition to the issue of feature extraction, the design and choice of the classifier plays an essential role in the automatic classification of music [12,13].…”

mentioning

confidence: 99%

See 2 more Smart Citations

A Two-Step Approach for Classifying Music Genre on the Strength of AHP Weighted Musical Features

Chen

et al. 2018

Mathematics

View full text Add to dashboard Cite

Music is a series of harmonious sounds well arranged by musical elements including rhythm, melody, and harmony (RMH). Since music digitalization has resulted in a wide variety of new musical applications used in daily life, the use of music genre classification (MGC), especially MGC automation, is increasingly playing a key role in the development of novel musical services. However, achieving satisfactory performance of MGC automation is a practical challenge. This paper proposes a two-step approach for music genre classification (called TSMGC) on the strength of analytic hierarchy process (AHP) weighted musical features. Compared with other MGC approaches, the TSMGC has three strong points for better performance: (1) various musical features extracted from the RMH and the calculated entropy are comprehensively considered, (2) the weight of features and their impact values determined by AHP are applied on the basis of the Exponential Distribution function, (3) music can be accurately categorized into a main-class and further sub-classes through a two-step classification process. According to the conducted experiment, the result exhibits an accuracy rate of 87%, which demonstrates the potential for the proposed TSMGC method to meet the emerging needs of MGC automation.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

A Two-Step Approach for Classifying Music Genre on the Strength of AHP Weighted Musical Features

Chen

et al. 2018

Mathematics

View full text Add to dashboard Cite

show abstract

“…Since the seminal study of Tzanetakis and Cook (2002), MGC has been a popular topic in the MIR community (Fu et al, 2011;Knees and Schedl, 2013;Corrêa and Rodrigues, 2016). Despite the notable number of publications, about 500 according to Sturm (2012b), the field still presents open challenges nowadays, such as the illdefined concept of genre, which is vague, fuzzy and subject to human perceptions (Aucouturier and Pachet, 2003), or the questionable significance of machine learning approaches centered on reproducing "ground truth" of given datasets (Sturm, 2014).…”

Section: Music Genre Classificationmentioning

confidence: 99%

Machine learning for music genre: multifaceted review and experimentation with audioset

Ramírez

Flores

2019

J Intell Inf Syst

View full text Add to dashboard Cite

Music genre classification is one of the sub-disciplines of music information retrieval (MIR) with growing popularity among researchers, mainly due to the already open challenges. Although research has been prolific in terms of number of published works, the topic still suffers from a problem in its foundations: there is no clear and formal definition of what genre is. Music categorizations are vague and unclear, suffering from human subjectivity and lack of agreement. In its first part, this paper offers a survey trying to cover the many different aspects of the matter. Its main goal is give the reader an overview of the history and the current state-of-the-art, exploring techniques and datasets used to the date, as well as identifying current challenges, such as this ambiguity of genre definitions or the introduction of human-centric approaches. The paper pays special attention to new trends in machine learning applied to the music annotation problem. Finally, we also include a music genre classification experiment that compares different machine learning models using Audioset.

show abstract

“…To give every music object in our music dataset [6] similarity value with user preference vector, we compute the similarity value S between song vector and user preference vector using the following formula. (2) where M denotes song vector to be compared to user preference vector; A denotes user preference vector; n denotes the number of dimensions of song vector; m i denotes the ith dimension of M ; and a i denotes the ith dimension of A.…”

Section: Similarity Calculation Between Vectorsmentioning

confidence: 99%

Music Recommendation for Individual Music Preference

Shinohara¹,

Mizuno²

2019

IJCTE

View full text Add to dashboard Cite

Over the last several years, music streaming services have come in handy in our lives. Apple Music, Spotify, and Google Play Music is one of the most commonly used music streaming services. There are a number of studies about music recommendation system, one of the functions in music streaming services. Most of studies about music recommendation system express music features using music information extracted from song components. The way to express music features and to come up recommendations out of music features varies. In this paper, we consider couple of approaches that reflect users music preference including music features and construct our music recommendation model through those approaches. Our proposed method is to recommend music with user preference vector, which has users music preference, referring to the idea of contents-based filtering.

show abstract

A survey on symbolic data-based music genre classification

Cited by 66 publications

References 29 publications

A Two-Step Approach for Classifying Music Genre on the Strength of AHP Weighted Musical Features

A Two-Step Approach for Classifying Music Genre on the Strength of AHP Weighted Musical Features

Machine learning for music genre: multifaceted review and experimentation with audioset

Music Recommendation for Individual Music Preference

Contact Info

Product

Resources

About