Thassilo Gadermaier

Georg Heiler, Thassilo Gadermaier, Thomas Haider et al.

Good quality network connectivity is ever more important. For hybrid fiber coaxial (HFC) networks, searching for upstream high noise in the past was cumbersome and time-consuming. Even with machine learning due to the heterogeneity of the network and its topological structure, the task remains challenging. We present the automation of a simple business rule (largest change of a specific value) and compare its performance with state-of-the-art machine-learning methods and conclude that the precision@1 can be improved by 2.3 times. As it is best when a fault does not occur in the first place, we secondly evaluate multiple approaches to forecast network faults, which would allow performing predictive maintenance on the network.

Maarten Grachten, Carlos Cancino-Chacón, Thassilo Gadermaier

This demo paper introduces partitura, a Python package for handling symbolic musical information. The principal aim of this package is to handle richly structured musical information as conveyed by modern staff music notation. It provides a much wider range of possibilities to deal with music than the more reductive (but very common) piano roll-oriented approach inspired by the MIDI standard. The package is an open source project and is available on GitHub.

Thassilo Gadermaier, Gerhard Widmer

Quantitative analysis of commonalities and differences between recorded music performances is an increasingly common task in computational musicology. A typical scenario involves manual annotation of different recordings of the same piece along the time dimension, for comparative analysis of, e.g., the musical tempo, or for mapping other performance-related information between performances. This can be done by manually annotating one reference performance, and then automatically synchronizing other performances, using audio-to-audio alignment algorithms. In this paper we address several questions related to those tasks. First, we analyze different annotations of the same musical piece, quantifying timing deviations between the respective human annotators. A statistical evaluation of the marker time stamps will provide (a) an estimate of the expected timing precision of human annotations and (b) a ground truth for subsequent automatic alignment experiments. We then carry out a systematic evaluation of different audio features for audio-to-audio alignment, quantifying the degree of alignment accuracy that can be achieved, and relate this to the results from the annotation study.

Thassilo Gadermaier, Maarten Grachten, Carlos Eduardo Cancino Chacón

This paper describes a computational model of loudness variations in expressive ensemble performance. The model predicts and explains the continuous variation of loudness as a function of information extracted automatically from the written score. Although such models have been proposed for expressive performance in solo instruments, this is (to the best of our knowledge) the first attempt to define a model for expressive performance in ensembles. To that end, we extend an existing model that was designed to model expressive piano performances, and describe the additional steps necessary for the model to deal with scores of arbitrary instrumentation, including orchestral scores. We test both linear and non-linear variants of the extended model n a data set of audio recordings of symphonic music, in a leave-one-out setting. The experiments reveal that the most successful model variant is a recurrent, non-linear model. Even if the accuracy of the predicted loudness varies from one recording to another, in several cases the model explains well over 50% of the variance in loudness.

Maarten Grachten, Carlos Eduardo Cancino-Chacón, Thassilo Gadermaier et al.

Many people enjoy classical symphonic music. Its diverse instrumentation makes for a rich listening experience. This diversity adds to the conductor's expressive freedom to shape the sound according to their imagination. As a result, the same piece may sound quite differently from one conductor to another. Differences in interpretation may be noticeable subjectively to listeners, but they are sometimes hard to pinpoint, presumably because of the acoustic complexity of the sound. We describe a computational model that interprets dynamics---expressive loudness variations in performances---in terms of the musical score, highlighting differences between performances of the same piece. We demonstrate experimentally that the model has predictive power, and give examples of conductor ideosyncrasies found by using the model as an explanatory tool. Although the present model is still in active development, it may pave the road for a consumer-oriented companion to interactive classical music understanding.