Antialias Filter Training for Audio Down-sampling
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Solution Overview
Problem
Existing audio data processing methods for speech recognition often result in aliasing when down-sampling, leading to inaccurate speech recognition results due to the loss of essential audio information, as they do not adapt the sampling rate effectively for various noise characteristics.
Innovation Solution
A method and apparatus that utilize a pre-generated antialias filter, trained through a process involving initial antialias filters, training voice data, and speech recognition models to adjust the filter based on recognition results, ensuring accurate down-sampling and maintaining audio data integrity for speech recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If audio data is down-sampled using conventional methods, then the sampling rate is reduced, but aliasing occurs and audio information is lost
Solution Approach 1:
The patent applies preliminary action by training the antialias filter in advance using speech recognition loss as a guiding signal. The filter is pre-optimized to preserve speech recognition-relevant information before the actual down-sampling operation occurs, preventing information loss rather than correcting it afterward
Solution Approach 2:
The patent changes the parameters of the antialias filter by adjusting its frequency response characteristics based on speech recognition loss. The filter parameters are optimized to preserve frequencies and patterns that are most relevant for speech recognition, rather than using conventional fixed filter parameters
2Productivity
If conventional down-sampling is applied, then processing speed increases, but speech recognition accuracy decreases
Solution Approach 1:
The patent implements feedback by using speech recognition loss as a guiding signal to adjust the antialias filter parameters. The speech recognition system provides feedback about which frequency components are important, and this feedback is used to optimize the filter characteristics for preserving recognition accuracy
Solution Approach 2:
The patent changes the filter parameters based on speech recognition requirements. The antialias filter is configured with specific frequency response characteristics that are optimized for speech signals, allowing faster processing without sacrificing recognition accuracy
3Device complexity
If a fixed antialias filter is used, then device complexity is reduced, but adaptability to different noise characteristics is poor
Solution Approach 1:
The patent enables parameter changes in the antialias filter by optimizing its frequency response characteristics based on speech recognition loss. The filter can be reconfigured for different sampling rate conversions and noise conditions, providing adaptability without requiring completely different filter designs
Solution Approach 2:
The patent applies preliminary action by pre-training the antialias filter with speech recognition objectives in mind. The filter parameters are pre-optimized to handle various noise characteristics and speech patterns, enabling the system to adapt to different conditions without complex real-time adjustments
Data Source
AI summary
A method and an apparatus for processing audio data are provided. The method includes: acquiring a first piece of audio data; and processing the first piece of audio data based on an antialias filter, to generate a second piece of audio data, a sampling rate of the second piece of audio data being smaller than a sampling rate of the first piece of audio data; the antialias filter being generated by: inputting training voice data in a training sample into an initial antialias filter; inputting an output of an initial antialias filter into a training speech recognition model, and generating a training speech recognition result; and adjusting the initial antialias filter based on the training speech recognition result and a target speech recognition result of the training voice data in the training sample.