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    Wyświetlanie 1-3 z 3
    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
    Artykuł
    Tytuł:
    The biological function of musical performance features
    Autorzy:
    Podlipniak, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/637764.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Jagielloński. Wydawnictwo Uniwersytetu Jagiellońskiego
    Tematy:
    music performance features, adaptation, biological function, cooperation, evolution, emotions
    Opis:
    In the world of Western musicology music is regarded as possessing two main divisions. The first being performance features which consist of traits such as dynamics, and tempo, and a second known as structural features such as the arrangement of notes in time. The main differences between these divisions is that performance features are evolutionarily ancient, indiscrete and present in many sound expressions, whereas structural features are evolutionarily younger, discrete and music-specific. Performance features are tightly connected with motor activity and emotional processing. Despite the fact that performance features carry information about emotional states, they can also be used as tools of manipulation. It has been proposed that one biological function of these tools is to affect the minds of other animals in order to arouse the need for cooperation in them. It has also been suggested that music performance features are homologous with some prosodic features of speech which evolved as a communicative tool before language and music.
    Źródło:
    Rocznik Kognitywistyczny; 2015, 8
    1689-927X
    Pojawia się w:
    Rocznik Kognitywistyczny
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    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
    Artykuł
      Wyświetlanie 1-3 z 3

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