Inference-Adaptive Neural Steering for Real-Time Area-Based Sound Source Separation (2408.12982v1)

Abstract: We propose a novel Neural Steering technique that adapts the target area of a spatial-aware multi-microphone sound source separation algorithm during inference without the necessity of retraining the deep neural network (DNN). To achieve this, we first train a DNN aiming to retain speech within a target region, defined by an angular span, while suppressing sound sources stemming from other directions. Afterward, a phase shift is applied to the microphone signals, allowing us to shift the center of the target area during inference at negligible additional cost in computational complexity. Further, we show that the proposed approach performs well in a wide variety of acoustic scenarios, including several speakers inside and outside the target area and additional noise. More precisely, the proposed approach performs on par with DNNs trained explicitly for the steered target area in terms of DNSMOS and SI-SDR.

• The paper presents a novel deep learning approach that dynamically shifts the target area using phase adjustments for real-time audio separation.
• The method employs a multi-microphone setup with a uniform linear array to generate separation masks while maintaining signal fidelity.
• Evaluations using DNSMOS and SI-SDR metrics demonstrate improved noise suppression and robustness in varied acoustic scenarios.

Inference-Adaptive Neural Steering for Real-Time Area-Based Sound Source Separation

The paper "Inference-Adaptive Neural Steering for Real-Time Area-Based Sound Source Separation" presents a novel technique for dynamically adapting the target area in a spatially aware multi-microphone sound source separation algorithm. The authors, Martin Strauss, Wolfgang Mack, Maria Luis Valero, and Okan Köpüklü, have developed a method allowing the target region of a deep neural network (DNN) to be adjusted during inference without necessitating a retraining phase.

Methodology

The approach, termed Neural Steering, involves initially training a DNN to retain speech within a predefined angular region while suppressing sounds originating from outside this region. The key innovation enables shifting this target area by applying a phase shift to the microphone signals during inference. This phase shift effectively reorients the area of interest with minimal computational overhead, maintaining computational efficiency suitable for real-time applications.

In practical terms, the setup involves a uniform linear array (ULA) of microphones. The trained DNN generates a separation mask from the captured audio spectrum to isolate the target signal. By modulating the phase of the input audio signals, the effective region of interest (ROI) is shifted. The new boundaries of the ROI are derived through the manipulation of the phase angles, a process that maintains the fidelity of separation performance within the newly defined region.

Performance Evaluation

The efficacy of Neural Steering is measured across various test scenarios involving different configurations of target and interfering speakers, both with and without additional noise. The researchers employ power reduction (PR) heatmaps to visualize the separation quality in different spatial configurations. These heatmaps highlight the DNN's ability to effectively suppress signals outside the ROI while maintaining signal integrity within the target region.

Key metrics for performance evaluation include DNSMOS (Deep Noise Suppression Mean Opinion Score) and SI-SDR (Scale-Invariant Signal-to-Distortion Ratio), indicating the perceptual and quantitative improvements in the separated signals, respectively.

Empirical results demonstrate that the proposed approach performs comparably with baseline models specifically trained for the respective steered ROIs. This assertion is supported by detailed results, which show steady improvements in DNSMOS and SI-SDR across various test cases, thereby validating the practical utility and robustness of the presented method.

Implications and Future Directions

The implications of this research are significant, especially in contexts requiring dynamic audio source separation, such as virtual meetings or hybrid workspaces. The ability to adapt the target area dynamically without retraining the DNN considerably reduces the computational burden and enhances operational flexibility.

Theoretically, this method opens avenues for more adaptive audio processing systems, where models can be fine-tuned in real-time to react to changing acoustic environments. Practically, this innovation holds promise for applications in device-based audio capture systems, conference call setups, and other scenarios where background noise and speaker dynamics are constantly evolving.

Future work could extend this concept by integrating direction-of-arrival (DoA) estimation systems to provide continuous adaptation to moving sources. Additionally, extending the framework to more complex microphone configurations—such as circular arrays—could further mitigate issues like front-back ambiguity, thus enriching the spatial resolution and separation quality.

Conclusion

"Inference-Adaptive Neural Steering for Real-Time Area-Based Sound Source Separation" presents a compelling approach to enhancing real-time sound source separation. By allowing dynamic adaptation of the target area via phase shifts in microphone signals, the method offers a practical and computationally efficient solution. This innovation holds promise for various applications, and further exploration could expand its applicability and effectiveness in even more complex audio environments.