Predictive Directional Selective Fixed-Filter Active Noise Control for Moving Sources via a Convolutional Recurrent Neural Network

TL;DR — The paper introduces Predictive Directional Selective Fixed-Filter Active Noise Control (PD-SFANC), which uses a lightweight convolutional recurrent neural network to forecast the next-frame direction-of-arrival of moving noise sources and proactively select pre-trained control filters, achieving stable noise reduction above 15 dB across various trajectories.

Key contributions

• Proposes a predictive directional active noise control framework (PD-SFANC) that replaces reactive filter switching with proactive, multi-frame context-based filter pre-selection.
• Designs a compact CRNN architecture combining 2D CNN blocks, adaptive frequency pooling, and a GRU layer to predict the next-frame DoA through multi-class classification.
• Eliminates the requirement for online gradient-based filter adaptation and feedback error signals in real time, avoiding convergence delays and divergence risks.
• Demonstrates robust generalization across unseen room geometries, high reverberation times (RT60 up to 0.83s), and real-world noise types from UrbanSound8K.

Problem

Traditional active noise control systems designed for stationary sources fail when applied to time-varying noise positions like moving vehicles or vacuum cleaners. Adaptive algorithms like FxLMS suffer from slow convergence and divergence risks, while standard selective fixed-filter ANC (SFANC) and directional SFANC (D-SFANC) methods react with a one-frame lag because they only evaluate current or past frames without tracking spatial-temporal dynamics. Although dynamic factor graph approaches (DFG-SFANC) improve tracking, they rely on traditional signal processing techniques requiring delicate empirical parameter tuning. This delay during source transitions results in severe degradation of noise reduction performance and high-amplitude error fluctuations.

Method

The PD-SFANC system separates processing into a co-processor running at the frame rate and a real-time controller running at the sampling rate (16 kHz). The co-processor inputs $K=4$ consecutive frames of $J=4$ channel reference signals, processed via STFT into magnitude and frequency spectrograms that are concatenated along the channel and time axes into a tensor $\mathbf{R} \in \mathbb{R}^{2J \times F \times TK}$. This tensor passes through three 2D convolutional blocks (each containing a 2D convolution, group normalization, ReLU activation, and max pooling) operating across time-frequency dimensions. Adaptive average pooling reduces dimensionality along the frequency axis, and a Gated Recurrent Unit (GRU) with a hidden state size of 64 models inter-frame temporal dynamics. Finally, a fully connected layer with softmax activation outputs the class probabilities for $V=36$ discrete azimuth DoA categories sampled at $10^\circ$ resolution. The network is optimized using cross-entropy loss and the Adam optimizer.

The system utilizes a pre-trained library of 36 control filter vectors of length 1024, pre-calculated via the FxLMS algorithm using broadband bandlimited white noise up to 2 kHz for each discrete DoA grid point. During online inference, after a brief $K$-frame cold-start period, the CRNN predicts the DoA index of the upcoming frame $\hat{v}$, and the system proactively updates the control filter vector $\mathbf{w}$ to $\mathbf{w}^{[\theta_{\hat{v}}]}$ before the frame transition occurs. Concurrently, the real-time controller executes signal cancellation at the audio sampling rate using $y(n) = \mathbf{w}^T(n)\mathbf{r}(n)$ and error calculation $e(n) = d(n) - s(n) * y(n)$, completely decoupling high-level tracking latency from real-time anti-noise generation.

Experimental setup

Numerical simulations use a 16 kHz sampling rate, 4 cardioid reference microphones in a tetrahedral Sennheiser AMBEO VR Mic geometry (0.025 m diameter), 1 secondary source, and 1 error source. The CRNN datasets comprise 86,400 training samples, 9,600 validation samples, and 9,600 test samples per room-SNR subset, built using synthesized white noise and real recordings from UrbanSound8K convolved with room impulse responses via the image source method. Baselines include standard FxLMS (stepsize $1\times 10^{-2}$), D-SFANC, and DFG-SFANC (adjacent observation weight 0.02, observation length 2). Evaluation metrics include DoA classification accuracy, power spectral density (PSD), and averaged noise reduction level (NRL) over 0.5-s evaluation windows. The CRNN model contains 0.05 million parameters and requires 480.08 million MACs.

Results

The CRNN achieves high DoA classification accuracy across all test rooms, exceeding 90% accuracy at SNRs of 20 dB and above (e.g., 90.3% to 91.7% in Room $\prime_1$), with a minor drop to around 86.8%–87.9% at 10 dB SNR. In continuous movement evaluations under vacuum cleaner noise moving at a constant angular velocity of $10^\circ/\text{s}$, both DFG-SFANC and PD-SFANC maintain an average noise reduction level (NRL) above 15 dB for the majority of the duration, whereas D-SFANC suffers from high-amplitude performance drops due to a one-frame filter-switching lag, and FxLMS shows minimal noise reduction due to slow convergence. Under a more challenging sinusoidal time-varying rate trajectory between $50^\circ$ and $150^\circ$, PD-SFANC maintains stable, high noise reduction throughout the trajectory, while DFG-SFANC experiences significant performance drops around the 7th and 15th seconds due to difficulties tracking rapidly varying acceleration in reverberant environments.

Limitations

The evaluation is restricted to numerical simulations in simulated acoustic rooms and assumes single-source scenarios. The model relies on a discrete DoA grid resolution of $10^\circ$ (36 categories) and evaluates only horizontal plane movements at a fixed radius from the microphone array. Performance in multi-source acoustic environments, moving source distances involving significant Doppler shifts, or real-world physical hardware deployment tests are not assessed in the scope of this work.

Why read this

Researchers and audio engineers working on active noise control for non-stationary or mobile systems should read this paper to see how framing directional filter selection as a temporal classification and sequence prediction task bypasses the divergence and convergence bottlenecks of traditional adaptive filters like FxLMS.

Code

• https://github.com/Wang-Boxiang/PD-SFANC

Applications

Active noise control systems for vehicles, drones, and robotic vacuum cleaners operating in environments with moving noise sources.

Institutions

Nanyang Technological University, Northwestern Polytechnical University

Funding / 經費: Ministry of Education, Singapore

Related

• Active Noise Control With a Gain Constraint for Micro-Loudspeakers — same problem · relatedness 2.3/3
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• Ego-Noise-Aware Spatial Filtering for Reliable UAV Audition in Extreme Low-SNR Conditions — same problem · relatedness 1.9/3
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