Sound Waveform Distribution Mapping for Acoustic Model Gaps
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing sound synthesis technologies face challenges in accurately identifying and efficiently selecting sound waveforms for training acoustic models to compensate for lacking sound ranges, making it difficult to produce natural-sounding synthesized voices and instrument performances.
Innovation Solution
A method is introduced to display the characteristic distribution of sound waveforms used for training, allowing users to identify and select appropriate waveforms for training, thereby enhancing the training process of acoustic models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sound synthesis technology uses machine learning with acoustic models, then synthesized sounds can be generated, but it becomes extremely difficult to accurately ascertain the sound range that is lacking in the acoustic model and identify suitable training sound waveforms
Solution Approach 1:
The patent introduces a characteristic distribution map as an intermediary tool that visually represents the sound range coverage of the acoustic model. This map serves as a mediator between the complex acoustic model parameters and the user, enabling easy identification of lacking sound ranges without requiring deep expertise in acoustic model analysis.
Solution Approach 2:
The patent replaces the traditional manual analysis method (listening and comparing sounds) with an automated information processing system that generates characteristic distribution maps. This substitution transforms the complex task of acoustic model analysis into an automated graphical display process, making it easier to identify training needs.
2Reliability
If traditional sound synthesis methods are used, then the process is simple, but synthesized sounds lack natural pronunciation and quality
Solution Approach 1:
The characteristic distribution map acts as an intermediary that simplifies the interaction between users and the complex acoustic model training process. By visually displaying the sound range coverage, it enables users to easily identify and select appropriate training sound waveforms without needing to understand the underlying acoustic model complexity.
Solution Approach 2:
The patent implements a feedback mechanism where the characteristic distribution map provides visual feedback about the acoustic model's current capabilities and deficiencies. This feedback loop enables users to make informed decisions about training data selection, improving the naturalness of synthesized sounds while maintaining ease of operation.
3Reliability
If comprehensive training data is collected to improve acoustic model quality, then synthesized sound quality improves, but the time and resources required for training increase significantly
Solution Approach 1:
The patent extracts and displays only the critical information about sound range coverage in the characteristic distribution map. By focusing on the essential characteristics rather than processing all training data comprehensively, the system enables rapid identification of training needs without the time cost of analyzing complete datasets.
Solution Approach 2:
The patent enables partial training by allowing users to selectively add training sound waveforms for specific lacking sound ranges identified in the characteristic distribution map. This partial action approach improves model quality incrementally without requiring comprehensive retraining, thereby reducing time loss while still achieving quality improvement.
Data Source
AI summary
A method for displaying information relating to an acoustic model that is established by being trained using a plurality of sound waveforms so as to generate acoustic features includes acquiring characteristic distribution of the plurality of sound waveforms used for training of the acoustic model. A characteristic of the characteristic distribution is one or more sound waveform characteristics.