Adaptive Audio Tonality Filtering for Automated Tone Adjustment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Adjusting the tone and dynamic range of music signals requires manual effort and expertise, making it challenging for non-professional sound engineers.
Innovation Solution
A digital signal processing method that calculates an adaptive tonality control by specifying a desired weighting curve, using shape and level dependent filter magnitude responses to automate tone and dynamic range adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual tone and dynamic range adjustments are performed by sound engineers, then high quality sound processing is achieved, but the process requires professional expertise and significant manual effort
Solution Approach 1:
The system performs self-service by automatically analyzing the music signal and applying appropriate tone and dynamic range adjustments without requiring manual intervention from sound engineers. The processor independently determines filtering parameters and applies them to achieve professional-quality results automatically.
Solution Approach 2:
The system changes parameters dynamically by analyzing the input signal characteristics and automatically adjusting filtering parameters, gain settings, and compression ratios to achieve optimal tone and dynamic range control without manual parameter tuning.
2Manufacturing precision
If professional sound engineers manually adjust tone and dynamic range, then desired sound quality is achieved, but the process is time-consuming and requires specialized knowledge
Solution Approach 1:
The system performs preliminary action by pre-defining multiple filtering parameter sets corresponding to different musical instruments and voice types. When processing a signal, the system quickly selects and applies the appropriate pre-configured parameters, eliminating the need for time-consuming manual analysis and adjustment while maintaining professional sound quality.
Solution Approach 2:
The automated processing system performs self-service by independently analyzing the input signal, selecting appropriate processing parameters, and applying tone and dynamic range adjustments without requiring professional sound engineer intervention, thereby dramatically reducing processing time while maintaining quality.
3Ease of operation
If automated processing is implemented, then manual effort is reduced, but achieving high quality tone adjustments becomes more difficult
Solution Approach 1:
The system performs preliminary action by pre-configuring multiple filtering parameter sets that correspond to different musical instruments and voice types. These pre-defined parameters encapsulate professional sound engineering knowledge, allowing the automated system to achieve high-quality tone adjustments by simply selecting and applying the appropriate pre-configured set based on the input signal characteristics.
Solution Approach 2:
The system uses an intermediary approach by introducing a processor that automatically analyzes the input signal and selects from pre-defined filtering parameter sets. This intermediary layer bridges the gap between simple automated processing and professional manual adjustment, enabling high-quality results through automated selection and application of expertly-crafted parameters.
Data Source
AI summary
A signal processing method for processing audio signals and in particular for adjusting the tone of music signals is disclosed. The method provides adaptive tonality control. Here, a shape dependent filter magnitude response is calculated, a level dependent filter magnitude response is calculated, a result response is calculated based on the shape and level dependent filter magnitude responses, and an output signal is calculated by convolving an input signal with the result response. In particular, the calculation of the shape dependent filter magnitude response comprises at least the steps of calculating a magnitude spectrum of a signal, filtering the resulting magnitude spectrum using a first smoothing filter, applying a predetermined weighting curve to the spectrum, detrending the weighted spectrum by fitting a curve to said weighted spectrum and subtracting said fitted curve from said weighted spectrum, and inverting the resulting spectrum to form the shape dependent filter magnitude response.