Speech Recording Equalization Using Reference Spectral Shape
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Solution Overview
Problem
Speech recordings often suffer from inconsistent loudness and clarity due to speaker movement and varying distances from the microphone, leading to non-constant audio levels and quality.
Innovation Solution
An automatic equalization system that uses a reference spectral shape to adjust gain settings for an input signal, employing averagers with different time constants to identify short-term changes and maintain a stable spectral shape, ensuring consistent audio quality by comparing the input signal to a reference signal and applying calculated gain values through a filter system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automatic equalization is applied to maintain consistent audio quality, then audio quality and loudness consistency are improved, but device complexity increases due to multiple averagers and spectral analysis components
Solution Approach 1:
The system segments the spectral analysis into multiple averagers with different time constants (short, mid, long) that process different aspects of the audio signal independently. Each averager handles specific temporal characteristics, and their results are combined to achieve robust equalization. This segmentation allows the complex task of maintaining audio quality consistency to be divided into manageable computational stages.
Solution Approach 2:
The system dynamically adjusts gain settings for different frequency bands based on real-time spectral shape comparisons. The equalization parameters are not fixed but continuously adapted by comparing the input signal's spectral shape against a reference spectral shape, allowing the system to respond dynamically to changing audio conditions while maintaining overall quality consistency.
2Measurement precision
If multiple averagers with different time constants are used to capture short-term changes, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The measurement process is segmented into multiple parallel averagers, each with a specific time constant optimized for detecting different temporal characteristics of spectral changes. The short time constant averager captures rapid changes, while longer time constant averagers provide contextual baseline information. This segmentation enables precise measurement of spectral shape variations without requiring a single overly complex measurement system.
Solution Approach 2:
The system uses multiple averagers with time constants that may be longer than strictly necessary for detecting short-term changes. This excessive action ensures that no spectral variations are missed and provides a comprehensive view of spectral evolution, with the understanding that some computational resources are expended on redundant but reassuring measurements.
Data Source
AI summary
Systems and methods to automatically equalize coloration in speech recordings is provided. In example embodiments, a reference spectral shape based on a reference signal is determined. An estimated spectral shape for an input signal is derived. Using the estimated spectral shape and the reference spectral shape a comparison is performed to determine gain settings. The gain settings comprise a gain value for each filter of a filter system. Using gain values associated with the gain setting, automatic equalization is performed on the input signal.