Music is a multifaceted art form that requires a nuanced and comprehensive framework for analysis. This framework should encompass correlations among diverse musical attributes, including melody, harmony, rhythm, timbre, and dynamics. However, modeling the interaction between these attributes is anticipated to require substantial computational resources. In this study, we propose a Bayesian model to represent various musical attributes and explore its implementation and inference on a quantum computer. We show how to translate the Bayesian model to a quantum circuit and perform inference using the quantum rejection sampling algorithm on a quantum simulator. Through empirical evaluation utilizing a real-world dataset, we compare the performance of the quantum algorithm against classical rejection sampling. Our results reveal that the quantum method improves the acceptance rate of generated samples, thereby enhancing posterior probability estimates.

We demonstrate a toolchain for visualising detailed road traffic data from multimodal sensors consisting of (i) the automatic, real-time extraction of movement paths of road users and noteworthy events from traffic monitoring cameras or LIDAR sensors, (ii) extraction of audio events from individual microphones and microphone arrays, (iii) a spatial data managment system storing the extracted information together with a geographic information system (GIS), and (iv) a web based viewer allowing to interactively visualise all these data in the context of a high-definition digital twin of the traffic environment. This system enables the collection of a considerable amount of objective data on road use and can be used for planning changes to traffic facilities as well as for assessing changes in the traffic environment.

The RVC-CAL language is used for implementing dataflow process networks (DPNs), i.e., distributed systems of actors. The behavior of an actor is defined by a set of actions which can consume input tokens and produce output tokens. RVC-CAL DPNs can offer parallelism both at the level of actors and at the level of actions. To efficiently execute these models on a target hardware, it is important to generate parallel code based on the entire parallelism provided by these two levels. In this paper, we discuss criteria for the generation of parallel software from RVC-CAL models based on the potential parallelism of modeled behaviors. The approach considers both the coarse-grained (task-parallel) execution of actors using multithreading and the fine-grained (data-parallel) execution of their actions using the open computing language (OpenCL) or even a higher-level layer of OpenCL, namely SYCL. The methodology is validated by benchmarks on OpenCL abstracted hardware platforms. Based on the experimental results, the methodology is evaluated for efficiency (performance) in comparison with a pure multithreaded C++ approach and a well-known reference framework.

As of February 2016 Facebook allows users to express their experienced emotions about a post by using five so-called `reactions'. This research paper proposes and evaluates alternative methods for predicting these reactions to user posts on public pages of firms/companies (like supermarket chains). For this purpose, we collected posts (and their reactions) from Facebook pages of large supermarket chains and constructed a dataset which is available for other researches. In order to predict the distribution of reactions of a new post, neural network architectures (convolutional and recurrent neural networks) were tested using pretrained word embeddings. Results of the neural networks were improved by introducing a bootstrapping approach for sentiment and emotion mining on the comments for each post. The final model (a combination of neural network and a baseline emotion miner) is able to predict the reaction distribution on Facebook posts with a mean squared error (or misclassification rate) of 0.135.

Subject of this paper is an industrial gantry robot with elastic behavior. To suppress residual vibrations command shaping filters are widely used. These filters, however, change the shape and duration of the originally commanded trajectory. Therefore, a command shaping filter is proposed which is based on the inverse model of the flexible robot dynamics. To that end, a planar dynamics model of the robot is derived which is suitable for control design. It captures the relevant dynamics of vibrations including the non-minimum phase characteristics. Based on the inverse dynamics, a feedforward filter for vibration suppression is developed. The tracking error due to filter delay is reduced compared to other command shaping techniques. The non-minimum phase properties of the flexible dynamics are avoided by using a virtual reference point for plant inversion. Experimental results show the effectiveness of the proposed Inverse Model Command Shaper compared to other types of filters tuned for vibration suppression.

In this paper, we present madmom, an open-source audio processing and music information retrieval (MIR) library written in Python. madmom features a concise, NumPy-compatible, object oriented design with simple calling conventions and sensible default values for all parameters, which facilitates fast prototyping of MIR applications. Prototypes can be seamlessly converted into callable processing pipelines through madmom's concept of Processors, callable objects that run transparently on multiple cores. Processors can also be serialised, saved, and re-run to allow results to be easily reproduced anywhere. Apart from low-level audio processing, madmom puts emphasis on musically meaningful high-level features. Many of these incorporate machine learning techniques and madmom provides a module that implements some methods commonly used in MIR such as hidden Markov models and neural networks. Additionally, madmom comes with several state-of-the-art MIR algorithms for onset detection, beat, downbeat and meter tracking, tempo estimation, and chord recognition. These can easily be incorporated into bigger MIR systems or run as stand-alone programs.

Carbon fiber reinforced plastics are playing a key role for aircraft constructions nowadays as well as in the future because of the convenient ratio of strength to weight. Due to the growing requirements of this market, an automation of the production process is necessary. Because of the high unit volumes and accuracy required, the use of robots with increased work accuracy is essential. The German Aerospace Center has developed, in an internal cooperation, an end effector for the camera-based determination of its position and orientation in space. This article deals with the construction and structure of the end effector and first experimental results.

Multi-functional robot cells will play an important role in smart factories of the future.Equipped with flexible toolings, teams of robots will be able to realize manufacturing processes with growing complexity.However, to efficiently support small batch sizes and a multitude of process variants, powerful software tools are required.This paper illustrates the challenges that developers face in multi-functional robot cells, using the example of CFRP production.The vision of a new programming environment for such future flexible automation systems is sketched.

In this paper, we propose a new state-of-the-art particle filter (PF) system to infer the metrical structure of musical audio signals. The new inference method is designed to overcome the problem of PFs in multi-modal probability distributions, which arise due to tempo and phase ambiguities in musical rhythm representations. We compare the new method with a hidden Markov model (HMM) system and several other PF schemes in terms of performance, speed and scalability on several audio datasets. We demonstrate that using the proposed system the computational complexity can be reduced drastically in comparison to the HMM while maintaining the same order of beat tracking accuracy. Therefore, for the first time, the proposed system allows fast meter inference in a high-dimensional state space, spanned by the three components of tempo, type of rhythm, and position in a metric cycle.

The study of musical expression is an ongoing and increasingly data-intensive endeavor, in which machine learning techniques can play an important role. The purpose of this paper is to evaluate the utility of unsupervised feature learning in the context of modeling expressive dynamics, in particular note intensities of performed music. We use a note centric representation of musical contexts, which avoids shortcomings of existing musical representations. With that representation, we perform experiments in which learned features are used to predict note intensities. The experiments are done using a data set comprising professional performances of Chopin's complete piano repertoire. For feature learning we use Restricted Boltzmann machines, and contrast this with features learned using matrix decomposition methods. We evaluate the results both quantitatively and qualitatively, identifying salient learned features, and discussing their musical relevance.

Based on Brooks' subsumption architecture, we introduce a relational definition of a layered module architecture. We present two straightforward implementations following the declarative and the procedural meaning of such architectures. Based on these representations and the notion of state snapshots we then explain how to learn relational, discrete descriptions of asynchronous and possible non-deterministic behaviour. We conclude with an outlook of how relational learning can be used to optimise module behaviour, how to prune redundant connections and how to induce functionalities of higher level competence.

The similarity join has become an important database primitive to support similarity search and data mining. A similarity join combines two sets of complex objects such that the result contains all pairs of similar objects. Well-known are two types of the similarity join, the distance range join where the user defines a distance threshold for the join, and the closest point query or k-distance join which retrieves the k most similar pairs. In this paper, we investigate an important, third similarity join operation called k-nearest neighbor join which combines each point Of one point set with its k nearest neighbors in the other set. It has been shown that many standard algorithms of Knowledge Discovery in Databases (KDD) such as k-means and k-medoid clustering, nearest neighbor classification, data cleansing, postprocessing of sampling-based data mining etc. can be implemented on top of the k-nn join operation to achieve performance improvements without affecting the quality of the result of these algorithms. We propose a new algorithm to compute the k-nearest neighbor join using the multipage index (MuX), a specialized index structure for the similarity join. To reduce both CPU and I/O cost, we develop optimal loading and processing strategies.

The similarity join is an important database primitive which has been successfully applied to speed up applications such as similarity search, data analysis and data mining. The similarity join combines two point sets of a multidimensional vector space such that the result contains all point pairs where the distance does not exceed a parameter ε. In this paper, we propose the Epsilon Grid Order, a new algorithm for determining the similarity join of very large data sets. Our solution is based on a particular sort order of the data points, which is obtained by laying an equi-distant grid with cell length ε over the data space and comparing the grid cells lexicographically. A typical problem of grid-based approaches such as MSJ or the ε-kdB-tree is that large portions of the data sets must be held simultaneously in main memory. Therefore, these approaches do not scale to large data sets. Our technique avoids this problem by an external sorting algorithm and a particular scheduling strategy during the join phase. In the experimental evaluation, a substantial improvement over competitive techniques is shown.