Abstract
We present MixMash, an interactive tool to assist users in the creation of music mashups based on cross-modal associations between musical content analysis and information visualisation. Our point of departure is a harmonic mixing method for musical mashups by Bernardes et al. [1]. To surpass design limitations identified in the previous method, we propose a new interactive visualisation of multidimensional musical attributes-hierarchical harmonic compatibility, onset density, spectral region, and timbral similarity-extracted from a large collection of audio tracks. All tracks are represented as nodes whose distances and edge connections indicate their harmonic compatibility as a result of a force-directed graph. In addition, we provide a visual language that aims to enhance the tool usability and foster creative endeavour in the search for meaningful music mixes.

Abstract
In this paper, we discuss the design considerations and goals of an interactive digital musical instrument (DMI) for novice pianists. It aims to promote the practice of melodic improvisation by guiding pianists in the selection of notes. The DMI includes two major components: a generative jazz model (software) and an illuminating keyboard controller (hardware). Visual feedback in the form of illuminated keyboard controller keys guides notes selection. Illuminated keys correspond to scale degrees that comply with a harmonic structure generated on-the-fly. The generative nature of the engine allows high degrees of novelty while guaranteeing a structurally-coherent harmonic structure anchored in the blues/jazz idiom. Preliminary experimental results inform critical directions for future design iterations of the proposed system.

Abstract
In this paper, we advance an enhanced method for computing Harte et al.’s (2006) Harmonic Change Detection Function (HCDF), which aims to detect harmonic transitions in musical audio signals. Each of the HCDF component blocks is revisited in light of recent advances in harmonic description and transformation. To evaluate our proposal, we compute an exhaustive grid search to compare the multiple proposed algorithms and a large set of parameterizations across four large style-specific musical datasets. Our results show that the newly proposed methods and parameter optimization improve the detection of harmonic changes by 5.57% (f-score) with respect to previous methods. Furthermore, while guaranteeing recall values at >99%, our other method improves precision by 6.28%.

Abstract

Abstract
We present SyVMO, an algorithmic extension of the Variable Markov Oracle algorithm, to model and predict multi-part dependencies from symbolic music manifestations. Our model has been implemented as a software application named INCITe for computer-assisted algorithmic composition. It learns variable amounts of musical data from style-agnostic music represented as multiple viewpoints. To evaluate the SyVMO model within INCITe, we adopted the Creative Support Index survey and semi-structured interviews. Four expert composers participated in the evaluation using both personal and exogenous music corpus of variable size. The results suggest that INCITe shows great potential to support creative music tasks, namely in assisting the composition process. The use of SyVMO allowed the creation of polyphonic music suggestions from style-agnostic sources while maintaining a coherent melodic structure. © 2021, Springer Nature Switzerland AG.

Abstract
Mobile handheld devices, such as smartphones and tablets, have become some of the most prominent ubiquitous terminals within the information and communication technology landscape. Their transformative power within the digital music domain changed the music ecosystem from production to distribution and consumption. Of interest here is the ever-expanding number of mobile music applications. Despite their growing popularity, their design in terms of interaction perception and control is highly arbitrary. It remains poorly addressed in related literature and lacks a clear, systematized approach. In this context, our paper aims to provide the first steps towards defining guidelines for optimal sonic interaction design practices in mobile music applications. Our design approach is informed by user data in appropriating mobile handheld devices. We conducted an experiment to learn links between control gestures and musical parameters, such as pitch, duration, and amplitude. A twofold action-reflection protocol and tool-set for evaluating the aforementioned links-are also proposed. The results collected from the experiment show statistically significant trends in pitch and duration control gesture mappings. On the other hand, amplitude appears to elicit a more diverse mapping approach, showing no definitive trend in this experiment.