The aim of this thesis is to define a perceptual scale for the ‘Time-Frequency’ analysis of music signals. These three different contexts of performance allow the understanding of the variability and the rules governing the different ways tezeta is performed. in inversions of other scales (gelbatch technique) used when a musician does not have the opportunity to switch to the ‘normal’ tezeta scale. in performances of exercises preparing the musician to perform the song 3. in performances of the song Tezeta by 5 different musicians 2. This paper investigates the variability of the pentatonic anhemitonic scale tezeta, by analyzing the intervals constituting this scale in different contexts: 1. In the secular repertoire of the Amhara of Ethiopia, the intervals sizes present certain variability. Especially in a context of development of computer-assisted tools for the study of scales, it is important to take this variability into consideration, as it is significant regarding the way pitches are organized and conceptualized within a musical system. 1998 ACM Subject Classification H.5.5 Sound and Music Computing, J.5 Arts and Humanities– Music, H.5.1 Multimedia Information Systems, I.5 Pattern RecognitionĪmong the numerous traits characterizing non-Western musical performances, the variability of scales has intrigued researchers. ![]() Finally, we give an outlook on a user-oriented retrieval system, which combines the various retrieval strategies in a unified framework. For these three important classes, we give an overview of representative state-of-the-art approaches, which also illustrate the sometimes subtle but crucial differences between the retrieval scenarios. ![]() Using a classification scheme based on specificity and granularity, we identify various classes of retrieval scenarios, which comprise audio identification, audio matching, and version identification. Furthermore, we introduce a second classification principle based on gran-ularity, where one distinguishes between fragment-level and document-level retrieval. Here, high specificity refers to a strict notion of similarity, whereas low specificity to a rather vague one. Such strategies can be loosely classified according to their specificity, which refers to the degree of similarity between the query and the database documents. In this contribution, we discuss content-based retrieval strategies that follow the query-by-example paradigm: given an audio query, the task is to retrieve all documents that are somehow similar or related to the query from a music collection. In the case that such textual descriptions are not available, one requires content-based retrieval strategies which only utilize the raw audio material. Traditional retrieval strategies rely on metadata that describe the actual audio content in words. The rapidly growing corpus of digital audio material requires novel retrieval strategies for exploring large music collections.
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