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

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

Direct characterization of the native structure and mechanics of cyanobacterial carboxysomes

Carboxysomes are proteinaceous organelles that play essential roles in enhancing carbon fixation in cyanobacteria and some proteobacteria. These self-assembling organelles encapsulate Ribulose 1,5-bisphosphate carboxylase/oxygenase (Rubisco) and carbonic anhydrase using a protein shell structurally resembling an icosahedral viral capsid. The protein shell serves as a physical barrier to protect enzymes from the cytosol and a selectively permeable membrane to mediate transport of enzyme substrates and products. The structural and mechanical nature of native carboxysomes remain unclear. Here, we isolate functional β-carboxysomes from the cyanobacterium Synechococcus elongatus PCC7942 and perform the first characterization of the macromolecular architecture and inherent physical mechanics of single β-carboxysomes using electron microscopy, atomic force microscopy (AFM) and proteomics. Our results illustrate that the intact β-carboxysome comprises three structural domains, a single-layered icosahedral shell, an inner layer and paracrystalline arrays of interior Rubisco. We also observe the protein organization of the shell and partial β-carboxysomes that likely serve as the β-carboxysome assembly intermediates. Furthermore, the topography and intrinsic mechanics of functional β-carboxysomes are determined in native conditions using AFM and AFM-based nanoindentation, revealing the flexible organization and soft mechanical properties of β-carboxysomes compared to rigid viruses. Our study provides new insights into the natural characteristics of β-carboxysome organization and nanomechanics, which can be extended to diverse bacterial microcompartments and are important considerations for the design and engineering of functional carboxysomes in other organisms to supercharge photosynthesis. It offers an approach for inspecting the structural and mechanical features of synthetic metabolic organelles and protein scaffolds in bioengineering

Visualisation of Origins, Destinations and Flows with OD Maps

We present a new technique for the visual exploration of origins (O) and destinations (D) arranged in geographic space. Previous attempts to map the flows between origins and destinations have suffered from problems of occlusion usually requiring some form of generalisation, such as aggregation or flow density estimation before they can be visualized. This can lead to loss of detail or the introduction of arbitrary artefacts in the visual representation. Here, we propose mapping OD vectors as cells rather than lines, comparable with the process of constructing OD matrices, but unlike the OD matrix, we preserve the spatial layout of all origin and destination locations by constructing a gridded two‐level spatial treemap. The result is a set of spatially ordered small multiples upon which any arbitrary geographic data may be projected. Using a hash grid spatial data structure, we explore the characteristics of the technique through a software prototype that allows interactive query and visualisation of 105‐106 simulated and recorded OD vectors. The technique is illustrated using US county to county migration and commuting statistics