1/29/2024 0 Comments Polyphonic in music![]() These algorithms carry out their mathematical operations in the time–frequency domain in order to extract three basic characteristics of the musical signal: tone texture (timbre), rhythmic content (time, rhythm, pulse), and tonal content (pitch). The descriptors most commonly used in feature extraction are Mel Frequency cepstral coefficients (MFCCs), spectral rollof, spectral flux, zero crossing rate, and low-energy feature. 2004), most feature extraction tools use knowledge of the audio signal processing field ( Eronen 2009 Silla Jr et al. 2009), and despite efforts to find a new path ( Goulart 2012 Jennings et al. 2012 Ezzaidi and Rouat 2008 Guaus 2009 Panagakis et al. Many classification platforms have been proposed (Costa et al. Owing to the need to develop computational resources for the organisation of large digital music libraries, the importance of automatic music classification systems has grown considerably in recent times ( Pampalk et al. In this article, we show that the modularity is able to give relevant information to allow the categorisation of 120 musical signs labelled in percussive and symphonic music. We also note that these differences are related to musical choices that can establish important differences between musical styles. We observed that a greater or lesser homogeneity of the magnitudes of the signal transients is related to a higher or lower modularity of the audio-associated visibility network. Then, we measure the quality of the partitions of the network using the modularity and Louvain optimisation. Next we map this series onto a visibility graph, where the nodes are the points of the series, and the edges are defined by the visibility between each pair of points. To implement this methodology, we first calculate the variance fluctuation series in fixed-size windows of an audio stretch. This article proposes a method to numerically characterise the homogeneity of polyphonic musical signals through community detection in audio-associated visibility networks and to detect patterns that allow the categorisation of these signals into two types of grouping based on this numerical characterization.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |