Using text mining techniques for classical music scores analysis

Music Classification is a particular area of Computational Musicology that provides valuable insights about the evolving of compo- sition patterns and assists in catalogue generation. The proposed work detaches from former works by classifying music based on music score in- formation. Text Mining techniques support music score processing while Classification techniques are used in the construction of decision mod- els. Although research is still at its earliest beginnings, the work already provides valuable contributes to symbolic music representation process- ing and subsequent analysis. Score processing involved the counting of ascending and descending chromatic intervals, note duration and meta- information tagging. Analysis involved feature selection and the evalu- ation of several data mining algorithms, ensuring extensibility towards larger repositories or more complex problems. Experiments report the analysis of composition epochs on a subset of the Mutopia project open archive of classical LilyPond-annotated music scores

Big Chord Data Extraction and Mining

Harmonic progression is one of the cornerstones of tonal music composition and is thereby essential to many musical styles and traditions. Previous studies have shown that musical genres and composers could be discriminated based on chord progressions modeled as chord n-grams. These studies were however conducted on small-scale datasets and using symbolic music transcriptions. In this work, we apply pattern mining techniques to over 200,000 chord progression sequences out of 1,000,000 extracted from the I Like Music (ILM) commercial music audio collection. The ILM collection spans 37 musical genres and includes pieces released between 1907 and 2013. We developed a single program multiple data parallel computing approach whereby audio feature extraction tasks are split up and run simultaneously on multiple cores. An audio-based chord recognition model (Vamp plugin Chordino) was used to extract the chord progressions from the ILM set. To keep low-weight feature sets, the chord data were stored using a compact binary format. We used the CM-SPADE algorithm, which performs a vertical mining of sequential patterns using co-occurence information, and which is fast and efficient enough to be applied to big data collections like the ILM set. In orderto derive key-independent frequent patterns, the transition between chords are modeled by changes of qualities (e.g. major, minor, etc.) and root keys (e.g. fourth, fifth, etc.). The resulting key-independent chord progression patterns vary in length (from 2 to 16) and frequency (from 2 to 19,820) across genres. As illustrated by graphs generated to represent frequent 4-chord progressions, some patterns like circle-of-fifths movements are well represented in most genres but in varying degrees. These large-scale results offer the opportunity to uncover similarities and discrepancies between sets of musical pieces and therefore to build classifiers for search and recommendation. They also support the empirical testing of music theory. It is however more difficult to derive new hypotheses from such dataset due to its size. This can be addressed by using pattern detection algorithms or suitable visualisation which we present in a companion study

Problems and opportunities of applying data-& audio-mining techniques to ethnic music

[TODO] Add abstract here

Representing chord sequences in OWL

Chord symbols and progressions are a common way to describe musical harmony. In this paper we present SEQ, a pattern representation using the Web Ontology Language OWL DL and its application to modelling chord sequences. SEQ provides a logical representation of order information, which is not available directly in OWL DL, together with an intuitive notation. It therefore allows the use of OWL reasoners for tasks such as classification of sequences by patterns and determining subsumption relationships between the patterns. The SEQ representation is used to express distinctive pattern obtained using data mining of multiple viewpoints of chord sequences

Visualising Chord Progressions in Music Collections: A Big Data Approach

In the Digital Music Lab project we work on the automatic analysis of large audio databases that results in rich annotations for large corpora of music. The musicological interpretation of this data from thousands of pieces is a challenging task that can benefit greatly from specifically designed interactive visualisation. Most existing big music data visualisation focuses on cultural attributes, mood, or listener behaviour. In this ongoing work we explore chord sequence patterns extracted by sequential pattern mining of more than one million tracks from the I Like Music commercial music collection. We present here several new visual representations that summarise chord patterns according to chord types, chroma, pattern structure and support, enabling musicologists to develop and answer questions about chord patterns in music collections. Our visualisations represent root movement and chord qualities mostly in a geometrical way and use colour to represent pattern support. We use two individually configurable views in parallel to encourage comparisons, either between different representations of one corpus, highlighting complimentary musical aspects, or between different datasets,here representing different genres. We adapt several visualisation techniques to chord pattern sets using some novel layouts to support musicologists with their exploration and interpretation of the corpora. We found that differences between chord patterns of different genres, e.g. Rock & Roll vs. Jazz, are visible and can be used to generate hypotheses for the study of individual pieces, further statistical investigations or new data processing and visualisation. Our designs will be adapted as user needs are established through ongoing work. Means of aggregating, focusing and filtering by selected characteristics (such as key,melodic patterns etc.) will be added as we develop our design for the visualisation of chord patterns in close collaboration with musicologists

Using Automated Rhyme Detection to Characterize Rhyming Style in Rap Music

Imperfect and internal rhymes are two important features in rap music previously ignored in the music information retrieval literature. We developed a method of scoring potential rhymes using a probabilistic model based on phoneme frequencies in rap lyrics. We used this scoring scheme to automatically identify internal and line-final rhymes in song lyrics and demonstrated the performance of this method compared to rules-based models. We then calculated higher-level rhyme features and used them to compare rhyming styles in song lyrics from different genres, and for different rap artists. We found that these detected features corresponded to real- world descriptions of rhyming style and were strongly characteristic of different rappers, resulting in potential applications to style-based comparison, music recommendation, and authorship identification

On the Modeling of Musical Solos as Complex Networks

Notes in a musical piece are building blocks employed in non-random ways to create melodies. It is the "interaction" among a limited amount of notes that allows constructing the variety of musical compositions that have been written in centuries and within different cultures. Networks are a modeling tool that is commonly employed to represent a set of entities interacting in some way. Thus, notes composing a melody can be seen as nodes of a network that are connected whenever these are played in sequence. The outcome of such a process results in a directed graph. By using complex network theory, some main metrics of musical graphs can be measured, which characterize the related musical pieces. In this paper, we define a framework to represent melodies as networks. Then, we provide an analysis on a set of guitar solos performed by main musicians. Results of this study indicate that the presented model can have an impact on audio and multimedia applications such as music classification, identification, e-learning, automatic music generation, multimedia entertainment.Comment: to appear in Information Science, Elsevier. Please cite the paper including such information. arXiv admin note: text overlap with arXiv:1603.0497

Rhetorical Pattern Finding

In this paper, we research rhetorical patterns from a musicological and computational standpoint. First, a theoretical examination of what constitutes a rhetorical pattern is conducted. Out of that examination, which includes primary sources and the study of the main composers, a formal definition of rhetorical patterns is proposed. Among the rhetorical figures, a set of imitative rhetorical figures is selected for our study, namely, epizeuxis, palilogy, synonymia, and polyptoton. Next, we design a computational model of the selected rhetorical patterns to automatically find those patterns in a corpus consisting of masses by Renaissance composer Tomás Luis de Victoria. In order to have a ground truth with which to test out our model, a group of experts manually annotated the rhetorical patterns. To deal with the problem of reaching a consensus on the annotations, a four-round Delphi method was followed by the annotators. The rhetorical patterns found by the annotators and by the algorithm are compared and their differences discussed. The algorithm reports almost all the patterns annotated by the experts and some additional patterns. The algorithm reports almost all the patterns annotated by the experts (recall: 98.11%) and some additional patterns (precision: 71.73%). These patterns correspond to rhetorical patterns within other rhetorical patterns, which were overlooked by the annotators on the basis of their contextual knowledge. These results pose issues as to how to integrate that contextual knowledge into the computational model