Temporal Deformable Convolution for Audio-Video Sampling Unification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing audio-video multimodality-based deep learning models are limited to predefined sampling frequency input formats, requiring modification and retraining to accommodate different formats, and face challenges in collecting sufficient learning data due to format restrictions.
Innovation Solution
An apparatus and method for audio-video sampling frequency ratio unification using a temporal deformable convolutional network to adjust the sampling frequency ratio of audio and video signals to a constant value, allowing for the use of learning data with various combinations of sampling frequencies, and adjusting the convolutional receptive field accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a predefined sampling frequency input format is used in existing audio-video multimodality models, then the model structure remains simple and training is straightforward, but the model cannot accommodate different input formats and requires modification and retraining for each format
Solution Approach 1:
The patent changes the sampling frequency parameter dynamically by introducing a sampling frequency conversion module that converts audio sampling frequencies to match video sampling frequencies. This allows the model to handle various input formats (e.g., 16kHz audio with 30fps video, 44.1kHz audio with 60fps video) without modifying the core network structure, thus improving adaptability while maintaining structural simplicity
Solution Approach 2:
The patent introduces a sampling frequency conversion module as an intermediary component between the audio input and the main network. This module acts as a mediator that transforms audio signals to the required sampling frequency before they enter the main processing network, allowing the core model to remain unchanged while accommodating diverse input formats
2Quantity of substance
If a predefined sampling frequency input format is used, then the network structure remains fixed and easy to implement, but collecting sufficient learning data becomes difficult due to format restrictions
Solution Approach 1:
By implementing dynamic sampling frequency conversion, the system can accept audio data in various sampling frequencies (8kHz, 16kHz, 44.1kHz, 48kHz) and convert them to match the video sampling frequency. This dramatically expands the pool of usable training data that can be collected from diverse sources without being constrained by a single predefined format
3Reliability
If the sampling frequency ratio between audio and video is not unified, then various input formats can be accepted, but the model cannot effectively process the multi-modal data due to temporal misalignment
Solution Approach 1:
The patent dynamically adjusts the audio sampling frequency parameter to match the video sampling frequency, ensuring a unified sampling frequency ratio. This temporal alignment is achieved through the sampling frequency conversion module that resamples audio signals in real-time, allowing the model to reliably process multi-modal data without temporal misalignment issues
Solution Approach 2:
The system employs dynamic sampling frequency conversion rather than a fixed conversion ratio. The conversion adapts to different input combinations (e.g., converting 16kHz audio to 30fps video sampling rate, or 44.1kHz audio to 60fps video sampling rate), making the temporal synchronization flexible and adaptable to various scenarios while maintaining processing reliability
Data Source
AI summary
Disclosed herein are an apparatus and method for audio-video sampling frequency ratio unification, including memory configured to store at least one program, and a processor configured to execute the program, wherein the program is configured to perform receiving an audio signal and a video signal, adjusting a ratio of a sampling frequency of the audio signal to a sampling frequency of the video signal so that the sampling frequency ratio is constant based on a deep learning network, and outputting an adjusted audio signal and the video signal.