Note And Chord Detection With Harmonic Change Separation
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Solution Overview
Problem
Existing note and chord detection systems fail to accurately capture subtle harmonic transformations within complex musical structures, leading to oversimplification and manual labor in digital music production, which hampers user experience and automation capabilities.
Innovation Solution
A system employing a combination module for grouping notes and a separation module to discern harmonic changes, combined with a curve generation model for predicting optimal modulation curves, enhances note and chord detection and evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing note and chord detection systems are used to identify discrete musical events, then clearly defined and separated notes and chords can be detected, but subtle transformations of harmonic content within a group of notes cannot be detected
Solution Approach 1:
The patent segments the detection process into two distinct modules: a combination module that groups notes into potential chords, and a separation module that identifies subtle harmonic transformations within those groups. This segmentation allows the system to handle both clearly defined discrete notes and subtle harmonic transformations by applying different detection strategies to different aspects of the music data.
Solution Approach 2:
The patent introduces an intermediary representation called 'combined note events' that serves as a bridge between individual notes and chord-level analysis. This intermediary structure allows the system to maintain information about individual notes while also capturing their collective harmonic relationships, enabling detection of both discrete events and subtle transformations.
2Stability of the object's composition
If a single prolonged chord is interpreted as a continuous harmonic entity, then traditional sheet music representation is maintained, but subtle harmonic changes and note transformations over time are overlooked
Solution Approach 1:
The combination module performs preliminary grouping of notes into combined note events before the separation module analyzes them for harmonic transformations. This preliminary action establishes the continuous harmonic entity while preserving the individual note information needed to detect subtle transformations, preventing information loss before analysis occurs.
Solution Approach 2:
The patent transforms the static view of a prolonged chord into a dynamic representation by analyzing temporal relationships among notes within combined note events. The separation module detects how harmonic content evolves over time within the prolonged chord, capturing subtle transformations that would be invisible in a static continuous harmonic entity view.
3Ease of operation
If users manually create modulation curves for composed notes and chords, then high level of customization is achieved, but valuable time is wasted and the creative process is hindered
Solution Approach 1:
The system performs self-service by automatically generating modulation curves based on the detected notes and chords without requiring manual user input. The curve generation model analyzes the musical context and produces appropriate modulation curves autonomously, eliminating the time-consuming manual task while preserving customization through adjustable parameters.
Solution Approach 2:
The system uses feedback from user interactions and musical context to generate appropriate modulation curves. The curve generation model learns from the detected harmonic transformations and temporal relationships, producing curves that adapt to the specific musical piece while reducing manual effort. Users can provide feedback to refine the automation further.
Data Source
AI summary
Methods and systems for the expressive and dynamic detection and evaluation of notes and chords in digital music production software are disclosed. In one embodiment, the method comprises a series of modules designed to extract and group notes into combinations based on several adjustable and scalable parameters. Furthermore, the method comprises the automatic integration of modulation curve data into the resulting groupings of notes, which can be achieved either through manual assistance or through the application of a predictive model.