We consider the problem of subgenre classification in music datasets. We propose an adaptation of association analysis, a technique to explore the inherent relationships among data objects in a problem domain, to capture subgenres’ char- acteristics through acoustical features. We further propose to use those characteristics to engage in a pairwise comparison among subgenres when classifying a new music piece. The initial investigation on our approach is examined through empirical experiments on a number of music datasets. The results are presented and discussed, with various related issues addressed
Previous work done in genre recognition and characterization from symbolic sources (monophonic melod...
We examine performance of different classifiers on different audio feature sets to determine the gen...
Abstract We present an algorithm that predicts musical genre and artist from an audio waveform. Our ...
We consider the problem of subgenre classification in music datasets. We propose an adaptation of as...
In this thesis, we investigate the problem of automatic music genre classification in the field of M...
We consider the genre classification problem in Music Information Retrieval and report our initial i...
Music genre meta-data is of paramount importance for the organization of music reposito-ries. People...
In the field of artificial intelligence, supervised machine learning enables us to try to develop au...
Music classification is a key ingredient for electronic music distribution. Because of the lack of s...
This paper introduces the AcousticBrainz Genre Dataset, a large-scale collection of hierarchical mul...
Given the huge size of music collections available on the Web, automatic genre classification is cru...
Abstract. Much work is focused upon music genre recognition (MGR) from audio recordings, symbolic da...
In this letter, we present different approaches for music genre classification. The proposed techniq...
[[abstract]]With the popularity of multimedia applications, a large amount of music data has been ac...
Using data mining techniques, including co-occurrence analysis, for the purpose of discovering simil...
Previous work done in genre recognition and characterization from symbolic sources (monophonic melod...
We examine performance of different classifiers on different audio feature sets to determine the gen...
Abstract We present an algorithm that predicts musical genre and artist from an audio waveform. Our ...
We consider the problem of subgenre classification in music datasets. We propose an adaptation of as...
In this thesis, we investigate the problem of automatic music genre classification in the field of M...
We consider the genre classification problem in Music Information Retrieval and report our initial i...
Music genre meta-data is of paramount importance for the organization of music reposito-ries. People...
In the field of artificial intelligence, supervised machine learning enables us to try to develop au...
Music classification is a key ingredient for electronic music distribution. Because of the lack of s...
This paper introduces the AcousticBrainz Genre Dataset, a large-scale collection of hierarchical mul...
Given the huge size of music collections available on the Web, automatic genre classification is cru...
Abstract. Much work is focused upon music genre recognition (MGR) from audio recordings, symbolic da...
In this letter, we present different approaches for music genre classification. The proposed techniq...
[[abstract]]With the popularity of multimedia applications, a large amount of music data has been ac...
Using data mining techniques, including co-occurrence analysis, for the purpose of discovering simil...
Previous work done in genre recognition and characterization from symbolic sources (monophonic melod...
We examine performance of different classifiers on different audio feature sets to determine the gen...
Abstract We present an algorithm that predicts musical genre and artist from an audio waveform. Our ...