Enhancing Video Content with AI-Driven Mood-Based Audio Recommendations: A Data-Centric UX Design Case Study



Overview



This case study showcases a comprehensive project aimed at enhancing the user experience in video content creation by leveraging data, machine learning, and AI. The study introduces a novel method for syncing audio with video through Natural Language Processing (NLP) to analyze text and find mood-based sound matches. This approach addresses the complex challenge of recognizing the context of video narratives, ultimately contributing to more immersive and emotionally resonant multimedia presentations.

Research Problem


Content creators face significant challenges in seamlessly syncing audio and video to evoke the desired mood and tone, hindered by time-consuming traditional methods, requiring expertise for efficient viewer engagement. Existing methods often lack the emotional nuance required for seamless audio-video harmonization.

This project seeks to address the following research question:

"How can text analysis be used to enhance matching sounds with the mood observed in video content?"

Overview of Methods
Comparison between Traditional and AI-Driven Audio Selection Processes: Streamlining Audio Matching with AI for Enhanced Efficiency and Precision.


Overview of Methods
Overview of methods: Diverse methods were applied across four stages, from the initial visioning in the problem space to the launch of the solution.

Research Objectives


To create a user-friendly tool that automatically analyzes textual input to determine the mood of a video and suggests audio tracks that fit that mood, thereby streamlining the audio-video editing process.

Stakeholders: Content creators, marketers, video editors, sound editors, MDDD students, lecturers, supervisors, designers, marketers, engineers, clients, and customers.

Methodologies Used: Natural Language Processing (NLP), TF-IDF, Word2Vec, co-reflection, heuristic evaluations, peer testing, expert reviews, and iterative design.

Research Objectives
Developing an AI-based NLP Tool to Enhance Content Creation and Improve Viewer Engagement

Situational Map
Situational Map of TAMBR (Sonic Branding) in the Data Economy: Diagram illustrates the intricate relationships and dynamics between stakeholders, institutions, and market trends, highlighting key tensions and values in the evolving landscape of sonic branding. It also emphasizes the balance between automation and creativity, the integration of audio-visual editing tools, and the influence of market research in shaping innovative approaches to branding through AI-driven methodologies.

Value Hierarchy
Value hierarchy translating values into design objectives and requirements, highlighting tension between values from a utilitarian perspective.

Exploration & Findings


The exploration phase involved extensive research and experimentation with various models and methodologies:

  1. Initial Attempts: Started with AI-driven object detection (YOLO) for sound alignment in video creation, which proved insufficient due to the lack of emotional connection.
  2. Shift to NLP Models: Transitioned to NLP models, focusing on TF-IDF and Word2Vec for mood detection based on text analysis. This approach significantly improved the alignment of soundtracks with video moods.
  3. Iterative Prototyping: Developed high-fidelity prototypes featuring user-friendly text input fields, autocomplete suggestions, relevance percentage displays, and infinite scrolling.
    High-Quality Visuals:
  • Wireframes and low-fidelity prototypes iteratively tested and refined based on feedback.
  • Final high-fidelity prototypes showcasing the tool’s interface and functionality.


Mood-Based Audio-Video Editing workflow
The mood board evolved through client collaboration, refining initial values like efficiency, control, and collaboration to a more focused set emphasizing innovation, creativity, inclusivity, and affordability. This iterative process, informed by customer feedback, ensured the final design aligned with both functional and emotional needs, supporting user empowerment, education, and artistic expression. Mood boards serve as a crucial tool in aligning visual direction and values with stakeholder expectations during the design process.

Mood-Based Audio-Video Editing workflow
Iteration 1: This low-fidelity prototype represents the foundational workflow of the audio-video editing tool. The process begins with users uploading their videos, followed by object detection within the video. Based on the detected objects, the tool then provides tailored sound recommendations, guiding users towards a more cohesive and contextually relevant audio-visual experience.

Mood-Based Audio-Video Editing workflow
High-Fidelity Prototype showcasing Google AI's and HEMD Algorithmic Patterns for enhanced, personalized object-driven music recommendations, empowering users with feedback, control, and explainability in AI-assisted decisions.

Mood-Based Audio-Video Editing workflow
Iterative Design Process illustrating the evolution from basic UI with limited features to an enhanced, simplified interface with added search and cloud-based editing functionalities.

Mood-Based Audio-Video Editing workflow
Refining the UI based on user feedback, introducing the 'AI Suggested Music' tab to recommend songs based on detected objects, while maintaining cloud-based project integration.


Methodology


1. Collection and Pre-processing:

  • Loaded and cleaned the dataset from a CSV file using pandas.
  • Combined relevant columns for keyword analysis.
  • Generated TF-IDF vectors and Word2Vec embeddings for the combined keywords.

2. Model Development:

  • TF-IDF: Simplifies mood detection by processing user-input keywords and comparing them with music metadata to calculate similarity scores.
  • Word2Vec: Converts words into dense numerical vectors, capturing semantic relationships and contextual meanings.

3. User Testing and Feedback:

  • Conducted surveys, interviews, and thinking aloud sessions to gather user feedback.
  • Utilized heuristic evaluations and peer reviews to refine prototypes.

4. Ethical Considerations:

  • Ensured transparency, user control, efficiency, and data minimization.
  • Adhered to ethical principles throughout the design process.

Technical Report Highlights:

  • Detailed explanation of the models used, data pre-processing steps, and performance metrics.
  • Integration of user feedback into model refinement and UI enhancements.

Mood-Based Audio-Video Editing workflow
Mood-Based Audio-Video Editing workflow: A conceptual process that uses NLP to integrate mood analysis into audio-video editing, matching audio tracks to video content based on specified or observed moods.

Mood-Based Audio-Video Editing workflow
Back-end process flowchart with TF-IDF and Word2Vec models for Music Recommendation System: This diagram illustrates the process of data preprocessing, keyword input, and text vectorization used to generate personalized music recommendations based on cosine similarity.

Impact & Results


The project led to significant user experience improvements, notably enhancing user satisfaction through the efficient and accurate audio recommendations. The interface was simplified, incorporating intuitive features such as autocomplete suggestions and relevance scores, making the tool more accessible and user-friendly.

Collaboration with stakeholders was a key component of the process, ensuring that the tool not only met their needs but also adhered to ethical standards. Regular feedback loops were established, allowing for continuous refinement of the tool's functionality and usability.

Moreover, the project highlighted the innovative potential of text-based mood detection in the realm of video content creation. It created substantial value for users by providing a robust solution that addresses the emotional aspects of audio-video synchronization, ultimately enhancing the overall creative process.


Mood-Based Audio-Video Editing workflow
Illustration of the Evolution of Stakeholder Values: From Initial Discovery through Iterative Design to Final Implementation and Resolution of Tensions.

Mood-Based Audio-Video Editing workflow
Iteration 3 testing evaluates accuracy, user interaction, and feedback integration to enhance AI-driven sound matching and contextual scene analysis.

Mood-Based Audio-Video Editing workflow
Iterations 4 & 5 focus on integrating NLP for mood detection, enhancing user interaction with contextually relevant audio suggestions, and refining keyword analysis through user feedback.

Overview of Mood-Based Audio Recommendations Using NLP for Video Content


Click here to open Prototype
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This infographic summarizes a research project focused on enhancing video content creation by syncing audio with video through the use of Natural Language Processing (NLP) for mood detection. The project outlines the methodology, analysis, and results, highlighting the potential of data-driven design to improve user experience and content alignment.

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High-Fidelity Prototypes Demonstrating AI-Driven Audio Integration Tools: These interfaces illustrate the advanced features of a platform where visuals and sounds are seamlessly merged with the assistance of AI, allowing users to easily upload media, select templates, and access AI-suggested music for enhancing video content.

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User Interface for AI-Driven Mood-Based Audio Suggestions: This prototype shows how users can interact with the video editor, where the AI prompts them to input moods or keywords for the video, enabling the tool to recommend audio tracks that match the visual content's emotional tone.

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Editing interface of a video creation tool showcasing the addition of mood-based keywords for enhanced content categorization and searchability.

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User interface for tagging videos with mood-based keywords, enhancing video organization and discoverability within a video editing platform.

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Integrating AI-powered music suggestions based on mood, enabling seamless audio selection that complements video content within the editing platform.

Reflection & Future Directions


The project encountered and overcame several challenges, including the initial limitations of object detection, which were successfully addressed by shifting to more sophisticated NLP models. The subjectivity and complexity of mood detection in speech presented another significant hurdle, which was mitigated through iterative testing and continuous user feedback.

Iterative learning, each test, feedback, and revision cycle improved the product and deepened my understanding of user needs and design principles. This adaptability allowed me to refine the tool in unexpected ways. Adapting to Insights, shifting from object detection to NLP for mood detection improved emotional context and compliance, highlighting the need for adaptability.

Looking ahead, there are plans to enhance the tool by integrating additional data types such as audio features and visual cues, by analyzing how emotions changes across in different video genres for better music matches, which could further improve the accuracy and relevance of recommendations. Exploring advanced embedding techniques like BERT is also on the agenda, aiming to achieve a deeper contextual understanding within the tool.

On a broader scale, the project prompted an analysis of the societal impacts of datafication and automation, particularly concerning jobs in video production. This reflection underscored the importance of supporting human expertise with AI-driven tools, ensuring that technology serves to augment rather than replace the creative process.


Conclusion


The "Optimizing Mood-Based Audio Recommendations for Video Content Using Text Analysis" project showcases the successful application of NLP and machine learning to enhance the creative process in video content creation. By prioritizing user experience, ethical considerations, and stakeholder collaboration, this project highlights the transformative potential of data-driven design in the evolving multimedia landscape.