Automatic Music Genre Classification Using a Hierarchical Clustering and a Language Model Approach

Langlois, Thibault; Marques, Gonçalo

doi:10.1109/mmedia.2009.42

Cited by 10 publications

(5 citation statements)

References 8 publications

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“…Or at least to rethink music genre classification "from the ground up" [27, p.4]. In contrast to classification, Langenlois and Marques [28] propose an unsupervised clustering. In particular, their clustering is based on song features.…”

Section: Related Workmentioning

confidence: 99%

Music Genres Reconsidered: Challenging Established Genres with a Data-driven Approach

Hotz-Behofsits¹,

Winkler²,

Wlömert³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Consumers widely use music genres (e.g., pop, rock) for finding the right products. However, they are commonly arbitrary, not-standardized, disputed, and closely related genres often overlap. In this paper, we challenge established music genres (e.g., pop, rock) by comparing them to an entirely data-driven approach.To this end, we use a unique data set of revealed user preferences to carry out a context-based artist similarity. This measure is used in turn to find high-density artist clusters. The contribution of this paper is twofold. First, we investigate the differences between established music genres and data-driven clustering. Second, we provide implications for researchers and practitioners.

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Section: Related Workmentioning

confidence: 99%

Music Genres Reconsidered: Challenging Established Genres with a Data-driven Approach

Hotz-Behofsits¹,

Winkler²,

Wlömert³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…• Speech/music discrimination or musical genre discrimination have been extensively explored with various methods, some examples of which are: [1] and [2] worked on the discrimination of a music dataset into different genre, using GMM and KNN for classification on various classical features. [3] worked on the differentiation between speech and music, introducing the uncertainty inside his classification with the use of the C-means algorithm.…”

Section: Previous Workmentioning

confidence: 99%

Sound classification in indoor environment thanks to belief functions

Labourey

Pellerin

Rombaut

et al. 2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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Sounds provide substantial information on human activities in an indoor environment, such as an apartment or a house, but it is a difficult task to classify them, mainly due to the variability and the diversity of realization of sounds in those environments. In this paper, sounds are considered as a class of information, to be mixed with other modalities (video in particular) in the design of ambient monitoring systems. As a consequence, we propose a classification scheme aimed at (i) exploiting the specificities of this modality with respect to others and (ii) leaving doubtful events for further analysis, so that the risk of errors is overall minimized. A dedicated taxonomy together with belief functions are proposed in this respect. Belief functions are an adapted way to face the variability of sounds, as they are able to quantify their impossibility to classify the signals when it differs too much from what is known by creating class of doubt. The algorithm is tested on a dataset composed of real-life signals.

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“…According to (10), we observe that human auditory response for frequency is log scale. The equation of Mel triangular bandpass filter is shown in (11) and the log energy of each triangular bandpass filter is shown in (12). Fig.…”

Section: Mfcc In Compressed Domainmentioning

confidence: 99%

“…To index various kinds of music, many musical attributes are considered such as rhythm, timbre (the singer or the musical instrument of a song) and music genre (R&B, Rock n Roll) [1,5,11,13,20,21]. The singing voice is the richest in a musical piece and the familiarity of a human voice is much more than associating an instrument with a musician [37].…”

mentioning

confidence: 99%

Content-based singer classification on compressed domain audio data

Tsai¹,

Huang²,

Liu³

et al. 2014

Multimed Tools Appl

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In this paper, we proposed a singer identification approach to automatically identify the singer of an unknown MP3 audio data. Differing from previous researches for singer identification in MP3 compressed domain, we use Mel-Frequency Cepstral Coefficients (MFCC) as the feature instead of MDCT (modified discrete cosine transform) coefficients. Although MFCC is often used in music classification and speaker recognition, it cannot be directly obtained from compressed music data such as MP3 format. We introduce a modified method for calculating MFCC vector in MP3 compressed domain. For describing the distribution of MFCC vector, the Gaussian mixture model (GMM) is applied. To find the nearest singer, we use maximum likelihood classification (MLC) to allot each input MFCC vector to its nearest group. The experimental result verifies the feasibility of the proposed approach.

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Automatic Music Genre Classification Using a Hierarchical Clustering and a Language Model Approach

Cited by 10 publications

References 8 publications

Music Genres Reconsidered: Challenging Established Genres with a Data-driven Approach

Music Genres Reconsidered: Challenging Established Genres with a Data-driven Approach

Sound classification in indoor environment thanks to belief functions

Content-based singer classification on compressed domain audio data

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