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Thanks to recent evolution of multimedia technology, to-day we often use computers as music recorders and players. Number of tunes stored in our computers is monotonically increasing, and therefore users often face difficulty while se-lecting tunes which they want to listen. To solve the problem, we aim visual selection of tunes based on their impr...
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In the field of computer music, melodic based forms of vocalizations have often been used as channels to access subject's queries and retrieve information from music databases. In this study, we look at percussive forms of vocalizations in order to retrieve rhythmic models entrained by subjects in Samba culture. By analyzing recordings of vocal per...
Seminar on Topics in Computer Music, Summer Semester 2017, RWTH Aachen University
Seminar on Topics in Computer Music, Summer Semester 2017, RWTH Aachen University
Citations
... It can represent various semantics by single icon; however, the technique is not music-specific. Music Icons [2] by Kolhoff et al., and MIST [3] by Oda et al., generate/select icon pictures according to musical features. However, these techniques do not take lyrics into account. ...
This paper presents "Lyricon", a technique that automatically selects multiple icons of tunes block-by-block, and effectively displays the icons. Here, Lyricon selects icons based on not only musical features, but also lyrical keywords. In other words, Lyricon can reflect not only the features of the tunes but also the story of lyrics on its icon selection. Users can understand both impression of the sounds and the content of the lyrics, and they can choose songs which is suitable for their feeling based on the visual impression of the icons. Besides, embedding Lyricon on GUIs of music players is convenient to play specific parts of songs.
This paper presents a framework for automatically generating speech-based interfaces for controlling virtual and augmented reality (AR) applications on wearable devices. Starting from a set of natural language descriptions of application functionalities and a catalog of general-purpose icons, annotated with possible implied meanings, the framework creates both vocabulary and grammar for the speech recognizer, as well as a graphic interface for the target application, where icons are expected to be capable of evoking available commands. To minimize user's cognitive load during interaction, a semantics-based optimization mechanism was used to find the best mapping between icons and functionalities and to expand the set of valid commands. The framework was evaluated by using it with see-through glasses for AR-based maintenance and repair operations. A set of experimental tests were designed to objectively and subjectively assess first-time user experience of the automatically generated interface in relation to that of a fully personalized interface. Moreover, intuitiveness of the automatically generated interface was studied by analyzing the results obtained through trained users on the same interface. Objective measurements (in terms of false positives, false negatives, task completion rate, and average number of attempts for activating functionalities) and subjective measurements (about system response accuracy, likeability, cognitive demand, annoyance, habitability, and speed) reveal that the results obtained by the first-time users and experienced users with the proposed framework's interface are very similar, and their performances are comparable with those of both the considered references.
Based on the dissolved gases analysis and grey relational theory, a fault diagnosis method for transformers is proposed. Fault diagnosis in transformers has several parameters such as the severity of fault and the kind of fault, a lot of typical fault samples are divided into several sets according to the fault type. The practical data are analyzed using grey relational analysis. The experiment showed that the proposed approach is available for the transformer fault diagnosis and has fault classified ability.
Today many people store music media files in personal computers or portable audio players, thanks to recent evolution of multimedia technologies. The more music media files these devices store, the messier it is to search for tunes that users want to listen to. We propose MusCat, a music browser to interactively search for the tunes according to features, not according to metadata (e.g. title, artist name). The technique firstly calculates features of tunes, and then hierarchically clusters the tunes according to the features. It then automatically generates abstract pictures, so that users can recognize characteristics of tunes more instantly and intuitionally. It finally visualizes the tunes by using abstract pictures. MusCat enables intuitive music selection with the zooming user interface.
