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In contrast, using standardized timbral features for classification often leads to overfitting resulting in worse performance. Our results indicate that the tonal features proposed in this thesis seem to robustly capture stylistic properties. Furthermore, we perform classification experiments according to historical periods ("eras") and composers. Using unsupervised clustering methods, we investigate the similarity of musical works across composers and composition years. On the basis of these novel audio features, we analyze audio recordings regarding musical style. Furthermore, we propose techniques for estimating the presence of specific interval and chord types and for measuring tonal complexity. Another method serves to visualize modulations regarding diatonic scales as well as scale types over the course of a piece. One of the algorithms estimates the global key of a piece by considering the particular role of the final chord. Based on these representations, we model specific concepts from music theory and propose algorithms to measure the occurrence of certain tonal structures in audio recordings. In the first step, we use signal processing techniques for computing chroma representations of the audio data. Our style analysis experiments focus on the fields of harmony and tonality. This thesis contributes with computational methods for realizing such analyses on comprehensive corpora of audio recordings.
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Musicologists usually analyze musical style in a manual fashion relying on scores. In particular, we focus on stylistic categories such as historical periods or individual composers. In this thesis, we approach such classification problems by discriminating subgenres within Western classical music. One application scenario is the classification of music recordings according to categories such as musical genres. In the area of Music Information Retrieval, researchers are developing automatic methods for organizing and browsing such collections.
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Nowadays, streaming services, download platforms, and private archives provide a large amount of music recordings to listeners. With the tremendously growing impact of digital technology, the ways of accessing music crucially changed. We evaluate the proposed models using a large dataset consisting of 75,000 songs for 30 different EDM subgenres, and show that the adoption of deep learning models and tempo features indeed leads to higher classification accuracy. And, we explore two fusion strategies, early fusion and late fusion, to aggregate the two types of tempograms.
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In this paper, we extend the state-of-art music auto-tagging model "short-chunkCNN+Resnet" to EDM subgenre classification, with the addition of two mid-level tempo-related feature representations, called the Fourier tempogram and autocorrelation tempogram. The state-of-art model is based on extremely randomized trees and could be improved by deep learning methods. While the classification task of distinguishing between EDM and non-EDM has been often studied in the context of music genre classification, little work has been done on the more challenging EDM subgenre classification. Furthermore, the classification can also help recommendation engines provide more targeted suggestions of music, and potentially help musicians to select genre labels for their music, and design music to better cater to preferences of their audiences based on previous reviews.Īlong with the evolution of music technology, a large number of styles, or "subgenres," of Electronic Dance Music(EDM) have emerged in recent years. These structures and review text information can allow us to understand how music audience (fans) perceive similar and different genres, and also assist in classifying different genres which share common-interest user communities, offering a more objective way in grouping music genres. In addition to identifying the clusters, we use Dependency Parsing and modified Term Frequency - Inverse Document Frequency to extract significant and unique features of each cluster.
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We then perform community detection and employ a network "averaging" method to obtain stable genre clusters, in order to analyse the structures of clusters both locally within each cluster and globally over the entire network. We model the relationships between genres using a user-oriented network, based on the written reviews. Using a dataset of more than 90,000 metal music reviews written by over 9,000 users in a period of 15 years, we analyse the genre structure of metal music with the aid of review text information.