Auflistung nach Schlagwort "Music"
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- KonferenzbeitragEvaluating Real-Time Pitch Estimation Algorithms for Creative Music Game Interaction(INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Meier, Peter; Schwär, Simon; Krump, Gerhard; Müller, MeinardMusic-based games are an important genre in the gaming community and have become increasingly popular with games like SingStar and Guitar Hero. These types of games are usually based on reactive game mechanics, where the player must hit a certain note at a certain time in order to score points. In this contribution, we present a game prototype that goes beyond purely music-reactive game mechanics and focuses more on the creative aspect of making music in games. In particular, we developed a jump-and-run game that can be controlled with a gaming controller but also uses the player’s singing voice to interact with the game world. To this end, we estimate the pitch of a microphone signal in real time and use it as a creative input to the game. This input can be used to control parts of the game world, for instance by singing and adding stair-like elements that allow the player to overcome obstacles and reach the end of a game level. With our game prototype, we demonstrate how game designers can incorporate musical challenges into a well-known game environment while motivating musicians to creatively explore and practice their musical skills. Furthermore, motivated by our game prototype, we evaluate different real-time pitch estimation algorithms using common MIR metrics on a publicly available dataset to analyse what works best for our gaming scenario.
- Textdokument"Hardness" as a semantic audio descriptor for music using automatic feature extraction(INFORMATIK 2017, 2017) Czedik-Eysenberg, Isabella; Knauf, Denis; Reuter, ChristophThe quality of "hardness" in music is an attribute that is most commonly associated with genres like metal or hard rock. However, other examples of music raise the question of whether there is a genre-independent general dimension of "hardness" that can be obtained from the signal automatically based on psychoacoustical features. In listening experiments 40 subjects were asked to rate 62 music excerpts according to their hardness. Using MATLAB toolboxes, a set of features covering spectral and temporal sound properties was obtained from the stimuli and investigated in terms of their correlation with the subjective ratings. By means of multiple linear regression analysis a model for musical hardness was constructed which shows a correlation of r = 0.86 with the experimental results. This proposes musical hardness as a useful high level descriptor for analysing collections of music. In ongoing experiments the fitness of this model is being further evaluated.
- KonferenzbeitragMo. Gemeinsam Musik erleben(Mensch & Computer 2011: überMEDIEN|ÜBERmorgen, 2011) Lenz, Eva; Laschke, Matthias; Hassenzahl, Marc; Lienhard, SébastienMusik hat eine ausgeprägte soziale Funktion. Besonders das gemeinsame Musikhören spielt dabei eine herausgehobene Rolle. Interessanterweise nimmt aber gängige Technik zum Abspielen von Musik die speziellen Anforderungen eines gemeinsamen Musikerlebnisses kaum auf. Die vorliegende Konzeptstudie präsentiert Mo, einen mp3-Player, dessen Funktionalität, Präsentation und Interaktion aus Überlegungen zu sozialen und emotionalen Aspekten des gemeinsamen Musikhörens abgeleitet wurden. Mo ist ein Beispiel für Experience Design, bei dem das intendierte, bedeutungsvolle Erlebnis der eigentliche Gegenstand der Gestaltung wird und das Produkt in all seinen Details der Materialisierung dieses Erlebnisses dient.
- WorkshopbeitragSmartphones as Drumsticks(Mensch & Computer 2011: überMEDIEN|ÜBERmorgen, 2011) Barth, Peter; Groß, Henning; Petri, RichardWe present a mobile application using two smartphones as drumsticks. Based on accelerometer data the type of the beat (left, middle, or right) is derived. Because acceleration sensors are device specific, a device-specific support vector machine on each device is used to detect beats and type of beats. To attain good recognition on a variety of devices more quickly, the training process is simplified and backed by a server. For the sound generation the two devices are connected and a configurable drum sound for each beat on either device is played on one of the devices.