Room Impulse Response Filter Estimation for Spatial Audio Accuracy
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Solution Overview
Problem
Existing methods for generating room impulse response (RIR) filters fail to accurately reflect spatial characteristics of a space due to the need for time-consuming and costly measurements, and conventional models are inadequate for uncontrolled environments with multiple sound sources.
Innovation Solution
A machine learning model is trained to estimate RIR filters using input signals from a predetermined location, incorporating responses from multiple sound sources, and utilizes white noise synthesis and loss functions to adjust and normalize filter components, enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional measurement methods are used to generate RIR filters, then measurement precision can be maintained, but the process becomes time-consuming and costly
Solution Approach 1:
The patent replaces conventional physical measurement methods with a machine learning-based estimation system. The processor uses a trained machine learning model to estimate RIR filters from input signals, substituting the mechanical measurement process with an intelligent computational approach that maintains accuracy while dramatically reducing time and cost requirements.
2Device complexity
If conventional models are used for RIR filter generation, then simplicity is maintained, but they become inadequate for uncontrolled environments with multiple sound sources
Solution Approach 1:
The patent segments the complex acoustic environment into multiple independent RIR components, each corresponding to a different sound source location. The machine learning model processes responses from multiple sound sources separately and combines them, enabling the system to handle uncontrolled environments with multiple simultaneous sound sources while maintaining model manageability.
Solution Approach 2:
The patent creates a composite RIR filter by combining multiple individual RIR filters corresponding to different sound source locations. This composite approach allows the model to represent complex acoustic environments with multiple sound sources by integrating simpler component filters, thereby improving reliability without requiring a completely new complex model architecture.
3Manufacturing precision
If RIR filters are generated to reflect spatial characteristics, then spatial audio reproduction quality is improved, but the generation process becomes more complex
Solution Approach 1:
The patent performs preliminary training of the machine learning model using RIR filters with known spatial characteristics. During this preliminary phase, the model learns to associate input signals with their corresponding spatial properties. When deployed, the pre-trained model can automatically estimate RIR filters that reflect spatial characteristics without requiring complex real-time processing, thus improving spatial audio reproduction while keeping the generation process manageable.
Data Source
AI summary
Disclosed is an audio signal processing device that estimates a room impulse response (RIR) filter of a space. The audio signal processing device may include a processor. The processor receives an input signal, and estimates, from the received input signal by using a machine learning model, an RIR filter representing a characteristic of a response transferred to a listener from a sound source located in a predetermined location in a space where the input signal is collected, wherein the predetermined location is a relative location determined based on the listener, wherein the machine learning model is trained to estimate a reference RIR filter from signals response associated with a reference RIR filter corresponding to the predetermined location and a plurality of additional RIR filters that represent characteristics of responses transferred to the listener from sound sources located in a space represented by the reference RIR filter.