Audio Source Separation with Noise-Adaptive Lightweight Networks
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Solution Overview
Problem
Audio devices with limited processing and power resources face challenges in providing effective noise-cancelling features in varying environments due to the need for large neural networks, leading to high latency and resource drain when connected to companion devices for data processing.
Innovation Solution
Implementing small-footprint specialized source separation networks (SFSSNs) on audio devices, selected by a companion device based on noise characteristics, to perform efficient audio source separation with reduced latency and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large neural networks are used for noise cancellation, then noise-cancelling performance is improved, but processing resources and power consumption increase
Solution Approach 1:
The system segments the neural network processing into two parts: a lightweight scene analysis network that runs locally on the audio device to identify noise conditions, and a larger source separation network that runs on the companion device. This segmentation allows the audio device to maintain low power consumption while still achieving effective noise cancellation through selective model execution.
Solution Approach 2:
The scene analysis network acts as an intermediary between the audio input and the source separation network. It processes the audio locally to identify noise characteristics, then selects and triggers the appropriate source separation model on the companion device. This intermediary approach enables the system to use large neural networks only when and where needed, reducing overall power consumption.
2Reliability
If large neural networks are used for noise cancellation, then noise-cancelling performance is improved, but device complexity increases
Solution Approach 1:
The system divides the processing complexity by separating the lightweight scene analysis function (running on the audio device) from the heavy source separation function (running on the companion device). This segmentation allows the audio device to remain simple and resource-efficient while still achieving high-performance noise cancellation through the distributed architecture.
Solution Approach 2:
The scene analysis network serves as an intermediary that bridges the gap between the resource-constrained audio device and the powerful companion device. It performs local preprocessing and model selection, enabling the audio device to leverage the companion device's computing power without requiring the audio device itself to have high processing capabilities.
3Reliability
If audio data is continuously transmitted to companion device for processing, then source separation quality is improved, but latency increases
Solution Approach 1:
The scene analysis network performs preliminary action by identifying noise conditions and selecting the appropriate source separation model before the actual source separation processing begins. This preliminary model selection is done locally and quickly, so when audio data needs to be transmitted to the companion device, the receiving end is already prepared with the correct processing model, significantly reducing latency.
Solution Approach 2:
The system uses periodic scene analysis to monitor noise conditions and update model selections at appropriate intervals rather than continuously transmitting and processing all audio data. This periodic approach maintains source separation quality by adapting to changing noise conditions while reducing overall processing latency and data transmission requirements.
Data Source
AI summary
Implementations of the subject technology provide systems and methods for providing audio source separation for audio input, such as for audio devices having limited power and/or computing resources. The subject technology may allow an audio device to leverage processing and/or power resources of a companion device that is communicatively coupled to the audio device. The companion device may identify a noise condition of the audio device, select a source separation model based on the noise condition, and provide the source separation model to the audio device. In this way, the audio device can provide audio source separation functionality using a relatively small footprint source separation model that is specific to the noise condition in which the audio device is operated.