Vehicle Noise Control with Split DSP-CPU Filter Updating
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Solution Overview
Problem
The high computational demands and complexity of road noise control systems in vehicles due to the wide frequency range and numerous channels required for noise cancellation lead to bottlenecks in computing power, affecting real-time performance and noise reduction effectiveness.
Innovation Solution
A noise control system utilizing a first processing unit, such as a DSP, for real-time signal acquisition and a second processing unit, like a CPU, for filter parameter updating, connected through inter-core communication to distribute computational tasks, allowing efficient noise control signal generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a road noise control system uses multiple channels and high-frequency processing to achieve effective noise cancellation across a wide frequency range, then noise reduction effectiveness is improved, but computing power requirements and system complexity increase significantly
Solution Approach 1:
The processing system is divided into two independent processing units: a first processing unit for acquiring reference acoustic signals and generating noise control signals, and a second processing unit for updating filter parameters. This segmentation allows each unit to specialize in specific tasks, reducing the computational burden on any single unit while maintaining overall system effectiveness across multiple frequency channels.
Solution Approach 2:
Filter parameters serve as an intermediary element that mediates between the reference acoustic signals and the noise control signals. By updating these parameters separately in the second processing unit, the system can adapt to changing noise conditions without requiring real-time complex computations during the noise cancellation process itself, thus reducing instantaneous system complexity.
2Reliability
If the system processes acoustic signals in real-time across multiple frequency channels, then noise control performance is improved, but computing power demand increases beyond available resources
Solution Approach 1:
The computational tasks are segmented into two distinct processing units with different functional responsibilities. The first processing unit handles signal acquisition and noise control signal generation, while the second processing unit handles filter parameter updates. This division allows the system to distribute computational workload and access different types of processing power (real-time signal processing vs. parameter optimization) without requiring a single overly powerful processor.
Solution Approach 2:
The second processing unit performs preliminary action by updating filter parameters in advance based on historical noise data and patterns. This preliminary parameter optimization reduces the computational complexity required during real-time noise cancellation operations, as the heavy lifting of parameter adaptation is completed beforehand, leaving only lighter signal processing tasks for the real-time execution phase.
3Reliability
If the system uses advanced algorithms for adaptive noise cancellation, then noise reduction effectiveness is improved, but real-time performance deteriorates due to high computational requirements
Solution Approach 1:
The algorithmic processing is segmented into two temporal phases: an offline parameter update phase handled by the second processing unit that analyzes historical data to optimize filter parameters, and an online signal processing phase handled by the first processing unit that executes real-time noise cancellation using the pre-optimized parameters. This segmentation separates computationally intensive tasks from real-time execution requirements, allowing complex algorithms to be applied without compromising real-time performance.
Solution Approach 2:
Complex algorithmic processing is performed in advance during the parameter update phase, where the second processing unit analyzes historical noise patterns and pre-computes optimized filter parameters. This preliminary action removes the computational burden from real-time execution, allowing the system to maintain both effective noise reduction through advanced algorithms and high real-time performance through simplified execution during the actual noise cancellation phase.
Data Source
AI summary
Disclosed are a noise control method and apparatus, a chip, and a vehicle. The method includes: acquiring at least one first reference acoustic signal and at least one error acoustic signal that correspond to a first noise reduction cycle; transmitting the at least one first reference acoustic signal and the at least one error acoustic signal to a second processing unit; updating filter parameters based on the at least one first reference acoustic signal and the at least one error acoustic signal to obtain first filter parameters, and returning the same to the first processing unit; and performing, based on the first filter parameters, filtering processing on at least one second reference acoustic signal corresponding to a second noise reduction cycle to obtain at least one noise control signal, and correspondingly transmitting the same to at least one sound source corresponding to the at least one second position.