Audio-Video Depression Detection via Multi-Head Attention Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for automatic depression detection using audio-video data often involve multiple steps and models, leading to deviations in object functions and accumulated errors, resulting in low accuracy of prediction results.
Innovation Solution
An automatic depression detection method that involves acquiring and preprocessing long-term audio and video files, extracting in-depth features using expanded convolution layers and 3D convolution with bidirectional long short-term memory networks, applying a multi-head attention mechanism for feature calculation, and aggregating these features through a feedforward neural network to input into a decision network for depression level prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple steps and multiple models are used to carry out prediction, then the diagnostic capability is enhanced, but errors are accumulated and accuracy of prediction result decreases
Solution Approach 1:
The patent merges multiple feature extraction processes and prediction models into a unified end-to-end depression detection system. Audio features, video features, and text features are extracted and processed through integrated neural network modules that share parameters and gradients, allowing the system to maintain enhanced diagnostic capability while reducing error accumulation through joint optimization of all components.
Solution Approach 2:
The system segments the depression detection task into distinct feature extraction modules (audio, video, text) that are processed independently but integrated through a unified loss function and end-to-end training framework. This segmentation allows specialized processing for each modality while maintaining overall system coherence and reducing error propagation through modular design.
2Reliability
If multiple steps and multiple models are used to carry out prediction, then the diagnostic capability is enhanced, but the device complexity increases
Solution Approach 1:
The patent implements a multi-functional neural network architecture where shared layers and parameters serve multiple purposes across different feature extraction tasks. The unified model processes audio, video, and text inputs through common computational blocks, reducing the number of separate components needed while maintaining enhanced diagnostic capability across all modalities.
Solution Approach 2:
Multiple feature extraction and prediction functions are merged into a single integrated system with unified training and optimization. This consolidation reduces the complexity of managing separate models while preserving the diagnostic benefits of multi-modal analysis through coordinated processing.
3Measurement precision
If manual feature extraction is performed, then the interpretation is accurate, but the time consumption increases
Solution Approach 1:
The patent replaces manual feature extraction processes with automated neural network-based feature extraction. Deep learning models automatically learn and extract relevant features from audio, video, and text inputs, eliminating the need for manual intervention while maintaining or improving extraction accuracy through data-driven feature discovery.
Solution Approach 2:
The system performs self-service feature extraction where the neural networks automatically identify and extract meaningful features from raw inputs without human intervention. The models learn feature representations during training and apply them autonomously during inference, significantly reducing time consumption while preserving extraction quality.
Data Source
AI summary
Disclosed is an automatic depression detection method using audio-video, including: acquiring original data containing two modalities of long-term audio file and long-term video file from an audio-video file; dividing the long-term audio file into several audio segments, and meanwhile dividing the long-term video file into a plurality of video segments; inputting each audio segment/each video segment into an audio feature extraction network/a video feature extraction network to obtain in-depth audio features/in-depth video features; calculating the in-depth audio features and the in-depth video features by using multi-head attention mechanism so as to obtain attention audio features and attention video features; aggregating the attention audio features and the attention video features into audio-video features; and inputting the audio-video features into a decision network to predict a depression level of an individual in the audio-video file.