Researchers at ASU have developed a method for accurately and efficiently estimating excitation patterns and loudness with real-time functionality. This method reduces the filter bank size by iteratively selecting only the perceptually relevant components of sound waves, such that the total neural activity is preserved and the general shape of the excitation pattern is initially captured. Critical bands are then chosen based on their relevancy to the general shape, resulting in greater accuracy of estimated excitation patterns and loudness measures with minimal computational overhead. This method can be applied to loudness measurement systems, perceptual loudness based volume control, loudness equalization systems, and digital audio encoding schemes.
- Audio Coding
- Cloud Voice
- Hearing Aids
- Speech Recognition
- Voice Over IP
Benefits and Advantages
- Accurate – Iterative reevaluation of critical bands results in greater accuracy of excitation patterns and loudness measurements.
- Efficient – Calculates final loudness estimate and intermediate quantities with minimal computational overhead.
- Versatile – Can be used for computationally demanding encoding schemes as well as rapid real-time streaming.
- Saves Power – Ideal for light-weight software processes or portable audio devices.
For more information about the inventor(s) and their research, please see