Music Symbol Recognition via Ink Segmentation and Grammar Graphs
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Solution Overview
Problem
Current on-line music recognition systems for handwritten music notations are inefficient and user-unfriendly, particularly in transforming strokes into symbolic representations during composition, due to complex processing requirements and limitations in recognizing music symbols.
Innovation Solution
A music symbol recognition apparatus that detects and pre-segments handwritten notations, groups them into graphical objects, determines music symbol candidates based on spatial relationships and graphical features, and applies grammar rules to form graphs, selecting the most representative graph based on symbol and spatial costs for efficient recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If on-line recognition systems transform strokes into symbolic representations directly while the document is being composed, then recognition speed and user-friendliness are improved, but processing complexity and computational load increase significantly
Solution Approach 1:
The system segments the handwritten music notation into multiple graphical objects (staff lines, note heads, stems, flags, beams) and processes each object type separately through specialized recognition rules. This segmentation allows the complex recognition task to be divided into manageable sub-tasks, reducing overall processing complexity while maintaining real-time recognition capability.
Solution Approach 2:
The system performs preliminary classification of graphical objects into categories (staff lines, note heads, stems, flags, beams) before detailed symbol recognition. This preliminary action organizes the input data structure, enabling more efficient processing in subsequent stages and reducing the computational burden during actual symbol transformation.
2Measurement precision
If complex processing is applied to recognize music symbols during composition, then recognition accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies different recognition rules and processing strategies tailored to each type of graphical object (staff lines, note heads, stems, flags, beams). Each object type has its own specialized recognition logic, which improves accuracy for each specific element while avoiding the overhead of applying complex general-purpose algorithms to all elements uniformly.
Solution Approach 2:
The system changes processing parameters based on the type of graphical object being recognized. Different spatial relationships, graphical features, and grammar rules are applied depending on whether the object is a note head, stem, flag, or beam. This parameter adaptation enables accurate recognition with optimized processing time for each object class.
3Measurement precision
If spatial relationships and grammar rules are applied to determine music symbol candidates, then recognition accuracy is improved, but computational complexity increases
Solution Approach 1:
The system segments the recognition process into distinct stages: graphical object detection, spatial relationship analysis, grammar rule application, and symbol candidate determination. This segmentation allows each stage to be optimized independently, reducing overall computational complexity while maintaining high recognition accuracy through systematic processing.
Solution Approach 2:
The system dynamically selects and applies appropriate grammar rules based on the detected graphical objects and their spatial relationships. The grammar rule application is adaptive, choosing only the relevant rules needed for the current musical context, which reduces computational complexity compared to applying all possible rules uniformly.
Data Source
AI summary
Disclosed are music symbol recognition apparatuses and methods that recognise music symbols from handwritten music notations. Various implementations may process handwritten music notations by segmenting the handwritten music notations into a plurality of elementary ink segments and then grouping the segments into graphical objects based on spatial relationships between the segments. One or more candidate music symbols may be determined for each graphical object, along with a symbol cost for each symbol, which represents a likelihood that the graphical object belongs to a predetermined class of symbols. The music symbol candidates may be parsed to form graphs based on grammar rules, and the graph most likely to represent the handwritten music notations may be selected for display or other use. The selection may be based on the symbol costs associated with each candidate and on spatial costs associated with the grammar rules that are applied to the candidates.