Temat: music features - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A Study on of Music Features Derived from Audio Recordings Examples – a Quantitative Analysis
Autorzy:: Dorochowicz, A.
Kostek, B.
Powiązania:: https://bibliotekanauki.pl/articles/178092.pdf
Data publikacji:: 2018
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: music genre
audio parametrization
music features
Opis:: The paper presents a comparative study of music features derived from audio recordings, i.e. the same music pieces but representing different music genres, excerpts performed by different musicians, and songs performed by a musician, whose style evolved over time. Firstly, the origin and the background of the division of music genres were shortly presented. Then, several objective parameters of an audio signal were recalled that have an easy interpretation in the context of perceptual relevance. Within the study parameter values were extracted from music excerpts, gathered and compared to determine to what extent they are similar within the songs of the same performer or samples representing the same piece.
Źródło:: Archives of Acoustics; 2018, 43, 3; 505-516
0137-5075
Pojawia się w:: Archives of Acoustics
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Automatic Genre Classification Using Fractional Fourier Transform Based Mel Frequency Cepstral Coefficient and Timbral Features
Autorzy:: Bhalke, D. G.
Rajesh, B.
Bormane, D. S.
Powiązania:: https://bibliotekanauki.pl/articles/177599.pdf
Data publikacji:: 2017
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: feature extraction
Timbral features
MFCC
Mel Frequency Cepstral Coefficient
FrFT
fractional Fourier transform
Fractional MFCC
Tamil Carnatic music
Opis:: This paper presents the Automatic Genre Classification of Indian Tamil Music and Western Music using Timbral and Fractional Fourier Transform (FrFT) based Mel Frequency Cepstral Coefficient (MFCC) features. The classifier model for the proposed system has been built using K-NN (K-Nearest Neighbours) and Support Vector Machine (SVM). In this work, the performance of various features extracted from music excerpts has been analysed, to identify the appropriate feature descriptors for the two major genres of Indian Tamil music, namely Classical music (Carnatic based devotional hymn compositions) & Folk music and for western genres of Rock and Classical music from the GTZAN dataset. The results for Tamil music have shown that the feature combination of Spectral Roll off, Spectral Flux, Spectral Skewness and Spectral Kurtosis, combined with Fractional MFCC features, outperforms all other feature combinations, to yield a higher classification accuracy of 96.05%, as compared to the accuracy of 84.21% with conventional MFCC. It has also been observed that the FrFT based MFCC effieciently classifies the two western genres of Rock and Classical music from the GTZAN dataset with a higher classification accuracy of 96.25% as compared to the classification accuracy of 80% with MFCC.
Źródło:: Archives of Acoustics; 2017, 42, 2; 213-222
0137-5075
Pojawia się w:: Archives of Acoustics
Dostawca treści:: Biblioteka Nauki

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