ToolChain*: Multimodal Analysis

• ToolChain$^*$ is a comprehensive multimodal framework that integrates deep learning models for audio transcription, acoustic event detection, and visual analysis.
• It processes video by concurrently extracting synchronized audio waveforms and image frames to generate unified JSON outputs through parallel deep learning pipelines.
• The modular design supports scalable clustering, summarization, and context-aware violence detection, making it ideal for surveillance and public safety applications.

This article describes a toolchain for comprehensive audio/video analysis that leverages deep learning-based multimodal approaches to extract, integrate, and analyze both audio and visual information from video content. The system is designed to support advanced applications such as clustering, summarization, and specific detection tasks like identifying riot or violent contexts, while maintaining modularity and extensibility for future enhancements.

1. Overview and Objectives

The toolchain is developed to overcome the challenges inherent in processing heterogeneous video data. Its primary objective is to fuse distinct analysis tasks—such as speech transcription, acoustic event detection, and visual object detection—into a unified framework, thereby allowing advanced decision-making based on joint audio and visual cues. In the specific use case of detecting riot or violent contexts, the system processes indicator signals from both modalities and combines them using methods such as keyword matching and embedding-based semantic fusion. This integration is intended to support robust context-aware applications in surveillance, public safety, and automated content moderation scenarios.

2. Architectural Components

The architecture is modular and divided into several well-defined stages:

1. Data Extraction:
   • Audio data is extracted from the input video into waveforms.
   • Visual data is obtained by extracting image frames, ensuring that both modalities are temporally aligned.
2. Modality-Specific Deep Models:
   • The audio pipeline utilizes models for Speech-to-Text (S2T), Acoustic Event Detection (AED), and Acoustic Scene Classification (ASC). For instance, Whisper is employed for S2T, while PANNs and an in-house ASC model (AIT-ASC) handle AED and scene classification, respectively.
   • The visual pipeline includes models for Visual Object Detection (VOD), Image Captioning (IC), and Video Captioning (VC), with DETR, VEDA, and SWINBERT serving as representative implementations.
3. Output Serialization:
   • Results from each deep learning task are formatted into JSON files, providing structured outputs (e.g., transcribed text, detected events with probabilities, and visual features or captions).
4. Application Layer:
   • At this level, the toolchain fuses the individual modality outputs. For example, the system embeds outputs from both audio and visual streams, clusters them for content analysis, or scans the aggregated text for context-specific keywords such as "gun," "explosion," or "scream" to flag violent situations.

The toolchain is implemented using microservices; each deep learning model is deployed in its own Docker container within an isolated Anaconda environment. This design promotes replacement or updating of individual components without affecting the entire system. A FastAPI back-end manages API calls, while a Streamlit front-end provides interactive visualization and control.

3. Processing Workflow and Data Fusion

The end-to-end workflow establishes efficient processing from raw video to final application output:

1. Data Extraction: A video file is processed to extract both the audio waveform and individual frames. Efficient extraction ensures that temporal synchronization is maintained between the modalities.
2. Parallel Inference: The audio data is simultaneously passed through the S2T, AED, and ASC models. In parallel, the visual data is processed by the VOD, IC, and VC models. This parallel execution minimizes latency and allows for scalable handling of large video volumes.
3. Result Serialization and Fusion: Each task produces a JSON file encapsulating its output. The application layer then aggregates these JSON outputs. For clustering tasks, embedding representations of the transcriptions, detected events, and captions are computed, and techniques such as t-SNE are applied for 2D visualization of content clusters. For riot or violent context detection, a predefined keyword list is employed; the system scans through the text generated by S2T, AED, ASC, VOD, IC, and VC to locate key phrases corresponding to high-alert conditions. Temporal analysis of these keywords supports the detection of dynamic changes in context over the video timeline.
4. Reporting and Analytics: The final outputs may include comprehensive summaries that integrate all detected elements—in descriptive text form for a holistic view of the video—or an indexed clustering of similar videos. These outputs are stored in an open text format (JSON) which simplifies downstream analysis and integration with other systems.

4. Applications, Flexibility, and Extendability

The toolchain is designed to accommodate multiple practical applications:

1. Audio/Video Clustering: By embedding outputs from both modalities and reducing dimensions via techniques like t-SNE, the toolchain can organize large volumes of videos by content or topic, supporting tasks such as content curation or trend analysis in surveillance datasets.
2. Comprehensive Audio/Video Summary: The fusion of different modalities yields holistic summaries that combine spoken content, visual object descriptions, acoustic events, and scene cues. For example, in a sports event video, the summary might include narrative elements about game play, crowd noise, and categorized scene types (e.g., "sport atmosphere").
3. Riot or Violent Context Detection: A specialized application employs keyword matching and temporal analysis to automatically detect and flag potential violent or riot situations. By monitoring the frequency and context of critical keywords over time, the system can generate visual alarms on a timeline to indicate periods of high risk.

The open, modular design ensures that the toolchain is flexible and readily extendable. New models for additional tasks (for example, anomaly detection or music event detection) can be integrated into the pipeline with minimal re-engineering. Moreover, the microservice architecture allows independent scaling of each component, facilitating deployment in diverse operational contexts.

5. Technical Implementation and Performance Considerations

The implementation leverages modern frameworks and containerization technologies to ensure robustness and scalability:

• Back-End and Front-End Services:

A FastAPI back-end orchestrates API requests to individual model containers, while a Streamlit front-end allows users to interactively visualize outputs and control processing parameters.

• Dockerized Microservices:

Each deep learning model is packaged in a Docker container, running in an isolated Anaconda environment. This isolation ensures that dependencies between models do not conflict and that models can be individually replaced or upgraded.

• Parallel Processing:

By running audio and visual inference tasks in parallel, the toolchain minimizes overall processing time and is capable of handling continuous streams or batch processing of large datasets.

• Output Efficiency:

The use of JSON as the output format across tasks promotes easy post-processing, integration with analytics pipelines, and reproducibility of experiments.

• Performance Metrics:

Although quantitative benchmarks such as accuracy or F1 scores are not provided, qualitative results indicate the system’s strong performance in processing complex, noisy real-world inputs. Evaluations on datasets like VCDB and DCASE 2021 suggest that the embedded clustering and summarization strategies yield useful visualizations and actionable summaries.

• Scalability and Adaptability:

The toolchain's design allows rapid adaptation to new application scenarios. Its containerized, microservice-based approach ensures it can be scaled out across distributed environments when processing large volumes of video data.

6. Conclusions and Future Directions

The presented toolchain represents a robust multimodal platform for comprehensive audio and video analysis. It seamlessly integrates state-of-the-art deep learning models for both low-level and high-level processing of media, and its flexible, modular architecture allows for easy expansion to new applications. By transforming raw video data into structured JSON outputs that can be clustered, summarized, or scanned for violent contexts, the system not only reduces human intervention in complex analysis tasks but also provides a scalable solution for domains such as public safety and surveillance.

Future work could expand the system’s capabilities by incorporating additional detection models or enhancing post-processing techniques (for instance, integrating continual learning for adaptive keyword selection). The open and adaptive nature of the toolchain ensures that it is well positioned to evolve alongside advances in deep learning and multimodal processing.

This multimodal analysis toolchain underscores the potential of integrating diverse deep learning tasks into a single framework to achieve advanced audio/visual analytics, with significant implications for both research and real-world applications in safety-critical contexts.