Audio Stream Alignment for Cloud Echo Cancellation
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Solution Overview
Problem
Cloud-based Echo Noise Cancellation Reduction systems face challenges in aligning audio streams due to significant and variable latency when processing audio signals from vehicles, leading to misalignment issues that affect echo and noise cancellation performance.
Innovation Solution
The method involves selecting the proper audio stream for processing using a loopback, Time Stamp, or Ping method to compensate for latency, ensuring accurate alignment of uplink and downlink audio signals by buffering, sequencing, and time-stamping, thereby enabling effective echo and noise cancellation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If audio streams are transmitted over a network to cloud-based ECNR processing, then processing capability is improved, but latency and misalignment between input and output streams increase
Solution Approach 1:
The patent applies preliminary action by pre-sending a portion of the output audio stream to the cloud before the corresponding input stream arrives. This allows the cloud-based ECNR system to begin processing earlier, compensating for network latency. The system sends output audio samples ahead of time and stores them in a buffer, so when the input stream arrives later, the processing can continue without significant delay.
Solution Approach 2:
The patent introduces an intermediary buffering mechanism that mediates between the input and output streams. The buffer stores pre-sent output audio samples and aligns them with the delayed input stream based on timestamp comparison. This intermediary structure allows the system to handle variable network latency while maintaining proper synchronization between input and output streams for ECNR processing.
2Power
If cloud-based processing is used, then computational power is improved, but stream alignment precision deteriorates due to variable latency
Solution Approach 1:
The patent implements feedback through timestamp comparison between input and output streams. Each audio sample carries a timestamp indicating when it was captured or generated. The system continuously compares these timestamps to determine the degree of misalignment and dynamically adjusts the buffering strategy. This feedback mechanism allows precise measurement and correction of alignment errors despite variable cloud processing latency.
Solution Approach 2:
The patent applies dynamics by making the buffering strategy adaptive rather than static. The system dynamically adjusts how many output samples to pre-send and how to align them with incoming input samples based on real-time latency measurements and timestamp comparisons. This dynamic approach allows the system to maintain high alignment precision even when cloud processing latency varies.
3Loss of time
If more audio samples are pre-sent to compensate for latency, then alignment is improved, but data transmission volume increases
Solution Approach 1:
The patent applies partial action by sending only a necessary portion of the output stream in advance, rather than the entire stream. The system calculates the minimum number of samples needed to compensate for expected latency and sends only that amount. This partial pre-sending approach provides sufficient alignment compensation while minimizing unnecessary data transmission.
Solution Approach 2:
The patent uses parameter changes by dynamically adjusting the pre-send window size based on network conditions and latency measurements. When latency is low, fewer samples are pre-sent; when latency is high, more samples are pre-sent. This parameter adjustment allows the system to optimize the balance between alignment quality and data transmission volume under different operating conditions.
Data Source
AI summary
A method for selecting an audio signal for alignment to compensate for the latency that is introduced by content being sent, such as from an end device, over a network to a cloud based or other computing environment located remote from the end unit. Audio that is processed in the cloud is also being sent back to the end device. Selection may be accomplished using a loop back method, a Time Stamp (TS) method or a Ping method. The Ping method allows incoming and outgoing audio signals to be selected and processed in the cloud.