Multi-Modal Audio Source Channelization for Overlapping Speakers
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Solution Overview
Problem
Existing audio source separation technologies struggle to accurately and efficiently isolate multiple target audio sources from complex, dynamic environments with overlapping audio sources such as noise, music, and reverberations, often requiring manual supervision and consuming significant computational resources.
Innovation Solution
A multi-modal audio source channelization system that utilizes a trained model to generate source-separated channel audio samples by combining audio and video signal features, reducing the need for manual supervision and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing audio source separation technologies are used to isolate multiple target audio sources from complex environments, then the separation accuracy can be improved, but the computational resources required increase significantly
Solution Approach 1:
The system segments the audio separation task by processing audio signals in overlapping frames with hop intervals, dividing the continuous processing into discrete manageable units. This allows efficient computational resource utilization while maintaining separation accuracy across the entire audio signal.
Solution Approach 2:
The system performs preliminary action by pre-processing audio signals through framing and windowing operations before separation. The audio signal is divided into frames with overlapping windows, and padding is applied to handle edge cases, preparing the data structure for efficient separation processing.
2Manufacturing precision
If existing audio source separation technologies are used to isolate multiple target audio sources, then the separation quality can be improved, but manual supervision requirements increase
Solution Approach 1:
The system implements self-service through automated speaker diarization that identifies and labels different speakers without manual intervention. The model automatically processes audio frames, separates sources, and generates labeled outputs, eliminating the need for manual supervision while maintaining high separation quality.
3Measurement precision
If complex processing methods are used to separate audio sources in dynamic environments, then the separation accuracy can be improved, but the processing time increases
Solution Approach 1:
The system applies periodic action by processing audio signals in regular frames with consistent hop intervals. This periodic processing approach allows the model to efficiently handle dynamic environments through structured, time-discretized processing while maintaining accuracy through the consistent application of separation operations.
Data Source
AI summary
Various embodiments of the present disclosure provide methods, apparatuses, systems, and/or devices that are configured to separate multi-source audio signal samples into discrete source separated channel audio samples using trained multi-modal audio source channelization models. Multi-source audio signal samples are difficult to separate because they can include multiple target audio sources (e.g., individual speakers) that are often inter-mixed and overlaid with other audio sources such as noise, music, reverberations, and other audio artifacts. The multi-modal audio source channelization models discussed herein are trained to generate source separated channel audio samples based on audio signal samples and on video signal samples.