Evolutionary Optimization of Music Performance Annotation

In this paper we present an enhancement of edit distance based music performance annotation. The annotation captures musical expressivity not only in terms of timing deviations but also represents e.g. spontaneous note ornamentation. To reduce the number of errors in automatic performance annotation, some optimization is essential. We have taken an evolutionary approach to optimize the parameter values of cost functions of the edit distance. Automatic optimization is desirable since manual parameter tuning is unfeasible when more than a few performances are taken into account. The validity of the optimized parameter settings is shown by assessing their error-percentage on a test set

Real Time Tracking and Visualisation of Musical Expression

Skilled musicians are able to shape a given piece of music (by continuously modulating aspects like tempo, loudness, etc.) to communicate high level information such as musical structure and emotion. This activity is commonly referred to as expressive music performance. The present paper presents another step towards the automatic high-level analysis of this elusive phenomenon with AI methods. A system is presented that is able to measure tempo and dynamics of a musical performance and to track their development over time. The system accepts raw audio input, tracks tempo and dynamics changes in real time, and displays the development of these expressive parameters in an intuitive and aesthetically appealing graphical format which provides insight into the expressive patterns applied by skilled artists. The paper describes the tempo tracking algorithm (based on a new clustering method) in detail, and then presents an application of the system to the analysis of performances by different pianists

Decomposing rhythm processing: Electroencephalography of perceived and self-imposed rhythmic patterns

Contains fulltext : 99240.pdf (publisher's version ) (Open Access)Perceiving musical rhythms can be considered a process of attentional chunking over time, driven by accent patterns. A rhythmic structure can also be generated internally, by placing a subjective accent pattern on an isochronous stimulus train. Here, we investigate the event-related potential (ERP) signature of actual and subjective accents, thus disentangling low-level perceptual processes from the cognitive aspects of rhythm processing. The results show differences between accented and unaccented events, but also show that different types of unaccented events can be distinguished, revealing additional structure within the rhythmic pattern. This structure is further investigated by decomposing the ERP into subcomponents, using principal component analysis. In this way, the processes that are common for perceiving a pattern and self-generating it are isolated, and can be visualized for the tasks separately. The results suggest that top-down processes have a substantial role in the cerebral mechanisms of rhythm processing, independent of an externally presented stimulus.12 p

Towards a gesture-sound cross-modal analysis

Abstract. This article reports on the exploration of a method based on canonical correlation analysis (CCA) for the analysis of the relationship between gesture and sound in the context of music performance and listening. This method is a first step in the design of an analysis tool for gesture-sound relationships. In this exploration we used motion capture data recorded from subjects performing free hand movements while listening to short sound examples. We assume that even though the relationship between gesture and sound might be more complex, at least part of it can be revealed and quantified by linear multivariate regression applied to the motion capture data and audio descriptors extracted from the sound examples. After outlining the theoretical background, the article shows how the method allows for pertinent reasoning about the relationship between gesture and sound by analysing the data sets recorded from multiple and individual subjects