Exploring annotations for musical pattern discovery gathered with digital annotation tools

Abstract

The study of inter-annotator agreement in musical pattern annotations has gained increased attention over the past few years. While expert annotations are often taken as the reference for evaluating pattern discovery algorithms, relying on just one reference is not usually sufficient to capture the complex musical relations between patterns. In this paper, we address the potential of digital annotation tools to enable large-scale annotations of musical patterns, by comparing datasets gathered with two recently developed digital tools. We investigate the influence of the tools and different annotator backgrounds on the annotation process by performing inter-annotator agreement analysis and feature-based analysis on the annotated patterns. We discuss implications for further adaptation of annotation tools, and the potential for deriving reference data from such rich annotation datasets for the evaluation of automatic pattern discovery algorithms in the future.

Innovative Construction of Reinforcement Learning Model for Information Fusion in Music Education

“…Tabuena [17] based on the mapping technology spatially mapped and classified multimodal data, which effectively proves the accuracy of multimodal fusion. Tomašević [18] uses the BOW model to generate file-level lyrics, which are then fused with the extracted audio and video features, and performs semantic classification. In short, multimodal fusion will play an increasingly important role in future research.…”

The Application of PCK Concept and Information Fusion-Oriented Multimedia Technology in Music Education

“…When asked, what type of music teachers these knowledge, skills, or attitudes are trained for in terms of the train-ing objectives of the curriculum, novice teachers generally think that it is to train all-round music teachers, and expert teachers think that the ideal job, the target system of the former teacher's curriculum should be a system that combines professionalism and teaching, and should emphasize the professional orientation of teachers. Therefore, regarding course objectives, novice teachers pay more attention to the comprehensive training of professional skills, while expert teachers emphasize the combination of professional skills and teaching ability [3].…”

Music Genre Classification Algorithm Based on Multihead Attention Mechanism

“…Similar to other classification methods, hierarchical classification methods also include several steps, such as feature extraction, data preprocessing, and automatic classification. However, the difference lies in the need to combine the different classification effects of different attributes based on the existing data in advance to construct a hierarchical model with a specific hierarchical structure and guarantee the classification effect [4]. e music genre automatic classification method proposed in [5] is based on related music features, including MFCC.…”