Visualising Singing Style under Common Musical Events Using Pitch-Dynamics Trajectories and Modified TRACLUS Clustering

Abstract

We present a novel method for visualising the singing style of vocalists. To illustrate our method, we take 26 audio recordings of A cappella solo vocal music from two different professional singers and we visualise the performance style of each vocalist in a two-dimensional space of pitch and dynamics. We use our own novel modification of a trajectory clustering algorithm called TRACLUS to generate four representative paths, called trajectories, in that two dimensional space. Each trajectory represents the characteristic style of a vocalist during one of four common musical events: (1) Crescendo, (2) Diminuendo, (3) Ascending Pitches and (4) Descending Pitches. The unique shapes of these trajectories characterize the singing style of each vocalist with respect to each of these events. We present the details of our modified version of the TRACULUS algorithm and demonstrate graphically how the plots produced indicate distinct stylistic differences between singers. Potential applications for this method include: (a) automatic identification of singers and automatic classification of singing styles and (b) automatic retargeting of performance style to add human expression to computer generated vocal performances and allow singing synthesisers to imitate the styles of specific famous professional vocalists.