Composer Recognition based on 2D-Filtered Piano-Rolls

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Convolution-based classification of audio and symbolic representations of music

We present a novel convolution-based method for classification of audio and symbolic representations of music, which we apply to classification of music by style. Pieces of music are first sampled to pitch–time representations (piano-rolls or spectrograms) and then convolved with a Gaussian filter, before being classified by a support vector machine or by k-nearest neighbours in an ensemble of classifiers. On the well-studied task of discriminating between string quartet movements by Haydn and Mozart, we obtain accuracies that equal the state of the art on two data-sets. However, in multi-class composer identification, methods specialised for classifying symbolic representations of music are more effective. We also performed experiments on symbolic representations, synthetic audio and two different recordings of The Well-Tempered Clavier by J. S. Bach to study the method’s capacity to distinguish preludes from fugues. Our experimental results show that our approach performs similarly on symbolic representations, synthetic audio and audio recordings, setting our method apart from most previous studies that have been designed for use with either audio or symbolic data, but not both

Symbolic Music Representations for Classification Tasks: A Systematic Evaluation

Music Information Retrieval (MIR) has seen a recent surge in deep learning-based approaches, which often involve encoding symbolic music (i.e., music represented in terms of discrete note events) in an image-like or language like fashion. However, symbolic music is neither an image nor a sentence, and research in the symbolic domain lacks a comprehensive overview of the different available representations. In this paper, we investigate matrix (piano roll), sequence, and graph representations and their corresponding neural architectures, in combination with symbolic scores and performances on three piece-level classification tasks. We also introduce a novel graph representation for symbolic performances and explore the capability of graph representations in global classification tasks. Our systematic evaluation shows advantages and limitations of each input representation. Our results suggest that the graph representation, as the newest and least explored among the three approaches, exhibits promising performance, while being more light-weight in training

Renaming that Tune: Aural Collage, Parody and Fair Use

Although the unauthorized use of sound recordings in derivative collage compositions may in some instances infringe on the copyright of a given composition or sound recording, such use may be protected under a fair use analysis typically accorded works of parody. Therefore this Comment will first provide some historical context for understanding aural appropriation as an evolving 20th century art form with parallels and antecedents in the visual arts. Next comes a discussion of how certain collage-based compositions may violate applicable copyright laws under the 1976 Copyright Act. This Comment will then explore whether the appropriation of pre-existing sound recordings may be justified under existing interpretations of fair use as defined in §107 of the 1976 Act. In particular, I will focus on the defense of fair use as it has historically been applied to works of parody, with an emphasis on two recent cases (Eveready Battery Co. v. Adolph Coors Co. and Acuff-Rose Music, Inc. v. Campbell) which appear to extend the parameters of the parody defense. After evaluating existing limitations in applying a fair use analysis to works of aural collage, this Comment will present some final observations, including suggestions offered by various commentators to protect the interests of copyright owners while simultaneously affording protection to collage composers