Multimodal Depression Detection via Spatio-Temporal Attention
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Solution Overview
Problem
Existing methods for depression detection lack accuracy due to feature extraction methods not being specifically directed towards depression tasks, and the combination of features does not fully consider the interactive relationships among different segments, resulting in low accuracy in depression prediction.
Innovation Solution
The proposed method involves extracting Fourier amplitude spectra from audio and video files, dividing them into segments, and using spatio-temporal attention networks to obtain horizontal features, which are then combined using a feature evolution pooling objective function to form a multimodal spatio-temporal representation for input into support vector regression for depression level prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple segments of features are extracted and simply spliced together, then the feature extraction process is straightforward, but the interactive relationship among various features is not considered and information is lost, resulting in low depression detection accuracy
Solution Approach 1:
The patent segments the long-time audio and video files into multiple short-time segments, and further divides the Fourier amplitude spectrum into spectral segments. This segmentation allows for detailed analysis of different time periods and frequency components, enabling the model to capture temporal variations in depression-related features while maintaining manageable complexity through structured processing of smaller units.
Solution Approach 2:
The patent implements nested attention mechanisms where segment-level attention features are combined with sequence-level attention features. The spatio-temporal attention network contains nested structures where temporal attention operates within spatial attention, and cross-modal attention operates within modal-specific attention. This nesting allows the model to capture interactions at multiple levels (segment-level and sequence-level) simultaneously, improving depression detection accuracy by considering both local and global feature relationships.
2Measurement precision
If feature extraction is performed without task-direction, then the processing is general-purpose, but the accuracy of depression detection is not high due to lack of task-specific feature selection
Solution Approach 1:
The patent applies local quality by using different processing strategies for different parts of the feature extraction pipeline. Audio features undergo Fourier transformation and spectral segmentation, while video features undergo frame extraction and spatial-temporal processing. Each modality receives tailored attention mechanisms (temporal attention for audio, spatio-temporal attention for video), and cross-modal attention is applied specifically to integrate complementary information. This task-directed local processing significantly improves depression detection accuracy compared to uniform general-purpose feature extraction.
3Loss of information
If all segment information is to be fully contained, then a complex combination method is needed, but simple splicing is easier to implement, creating a trade-off between information completeness and implementation simplicity
Solution Approach 1:
The patent implements feedback mechanisms through attention networks that compute relevance scores and weights based on the input features. The temporal attention network computes attention weights by comparing current segment features with historical segments, and the cross-modal attention network adjusts feature integration based on mutual relevance. This feedback-based weighting ensures that important information from all segments is preserved and emphasized, while less relevant information is appropriately downweighted, achieving complete information containment without requiring complex manual feature combination rules.
Data Source
AI summary
An automatic depression detection method includes the following steps of: inputting audio and video files, wherein the audio and video files contain original data in both audio and video modes; conducting segmentation and feature extraction on the audio and video files to obtain a plurality of audio segment horizontal features and video segment horizontal features; combining segment horizontal features into an audio horizontal feature and a video horizontal feature respectively by utilizing a feature evolution pooling objective function; and conducting attentional computation on the segment horizontal features to obtain a video attention audio feature and an audio attention video feature, splicing the audio horizontal feature, the video horizontal feature, the video attention audio feature and the audio attention video feature to form a multimodal spatio-temporal representation, and inputting the multimodal spatio-temporal representation into support vector regression to predict the depression level of individuals in the input audio and video files.