Data-driven local average room transfer function estimation for multi-point equalization.

Area of Science:

• Acoustics
• Signal Processing
• Machine Learning

Background:

• Multi-point room equalization (EQ) enhances sound quality over a wider area but requires extensive measurements.
• Measuring multiple room impulse responses (RIRs) for multi-point EQ is often impractical for end-users.
• Existing single-point EQ methods may not adequately address spatial variations in room acoustics.

Purpose of the Study:

• To develop a data-driven method for estimating a spatially averaged room transfer function (RTF) from a single-point RTF.
• To reduce the measurement burden associated with multi-point room equalization.
• To improve low-frequency room equalization performance using a computationally efficient approach.

Main Methods:

• A deep neural network (DNN) was trained using simulated room transfer functions (RTFs).
• The DNN was designed to estimate a spatially averaged RTF from a single-point RTF, focusing on the low-frequency region.
• The performance of the estimated RTF was evaluated within a finite impulse response (FIR) filter-based EQ framework.

Main Results:

• The DNN successfully learned a spatial smoothing operation, preserving spectral peaks while reducing notches present in single-point RTFs.
• The proposed method demonstrated improved room equalization performance compared to standard single-point EQ.
• While not achieving the performance of true multi-point EQ, the data-driven approach offered a significant enhancement.

Conclusions:

• A data-driven method effectively estimates spatially averaged RTFs from single measurements in the low-frequency range.
• This approach offers a practical improvement over single-point room equalization, reducing measurement complexity.
• The findings suggest potential for more accessible and effective room equalization techniques using machine learning.