Accompaniment Data Generation Using Chord Waveform Segmentation
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Solution Overview
Problem
Conventional automatic accompaniment apparatuses struggle to generate realistic accompaniment for ethnic instruments or instruments using unique scales, and they are limited to monophonic accompaniment phrases, failing to effectively utilize phrase waveform data including chords.
Innovation Solution
An accompaniment data generating apparatus that stores and processes phrase waveform data to separate and pitch-shift chord constituent notes, allowing for the generation of chord note phrases based on identified chord types and roots, enabling the creation of accompaniment data that can include chords and simulate more complex musical structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If MIDI or conventional automatic performance data is used to generate accompaniment, then the accompaniment can be generated for various music styles, but it is difficult to perform automatic accompaniment for ethnic instruments or instruments using peculiar scales and lacks realism of human live performance
Solution Approach 1:
The patent uses actual recorded waveform data from human performances as templates instead of generating synthetic MIDI data. By copying and processing real performance waveforms, the system preserves the natural characteristics and realism of human playing while maintaining adaptability to different musical contexts through pitch-shifting and time-stretching techniques
Solution Approach 2:
The system dynamically adjusts parameters such as pitch, tempo, and timing of the waveform data to match the input melody. By changing these parameters while preserving the original waveform characteristics, the system achieves both adaptability to different music styles and realism of human performance
2Device complexity
If phrase waveform data including chords is not utilized, then the system can maintain simplicity, but it is limited to monophonic accompaniment phrases and cannot generate complex chord structures
Solution Approach 1:
The patent separates chord waveform data into individual note components, allowing each note to be independently processed and recombined. This segmentation enables the system to generate complex chord structures from basic waveform elements without requiring completely new data for each chord type
Solution Approach 2:
The system uses a universal set of waveform data that can be applied to multiple chord types and musical contexts. By making the waveform library multi-functional and adaptable through parameter adjustment, the system achieves high versatility without proportionally increasing complexity
3Ease of manufacture
If conventional automatic accompaniment methods are used, then the processing can be straightforward, but the sound quality and musicality are limited compared to human performance
Solution Approach 1:
By copying actual human performance waveforms rather than synthesizing accompaniment data, the system achieves high sound quality that closely matches human performance while maintaining relatively simple processing through automated pitch and tempo adjustment
Data Source
AI summary
An accompaniment data generating apparatus has a storage device 15 for storing a set of phrase waveform data having a plurality of constituent notes which form a chord, and a CPU 9. The CPU 9 carries out a process for separating the set of phrase waveform data having a plurality of constituent notes which form a chord into a plurality of sets of phrase waveform data each having different one of the chord constituent notes, an obtaining process for obtaining chord information by which a chord type and a chord root are identified, and a chord note phrase generating process for pitch-shifting one or more of the separated phrase waveform data sets in accordance with chord type and combining the separated phrase waveform data sets including the pitch-shifted phrase waveform data to generate waveform data indicative of a chord note phrase as accompaniment data.