Titre de projet: Développement d’un système de recommandation musical basé sur l’analyse des émotions humaines via l’audio et le traitement du langage naturel pour une plateforme de streaming multilingue.

Project Abstract

This study presents the design, development, and evaluation of an advanced musical recommendation system that leverages emotion analysis from audio signals and natural language processing (NLP) to personalize streaming experiences across multiple languages. The core hypothesis is that user satisfaction and engagement can be significantly improved when recommendations are dynamically aligned with both expressed emotional states and contextualized preferences captured through language, sentiment cues, and listening history. The system integrates multimodal emotion detection from music and user-generated text (reviews, comments, search queries) using state-of-the-art deep learning architectures, including convolutional and transformer-based models for audio feature extraction and sentiment analysis, respectively. A unified representation of user mood, musical attributes, and semantic contexts is constructed through multimodal embedding techniques and temporal modeling to accommodate evolving taste profiles. To ensure scalability and real-time performance, the architecture employs a modular pipeline with streaming data ingestion, incremental learning, and online update mechanisms, while respecting privacy-preserving constraints via on-device personalization and differential privacy where appropriate. A multilingual NLP component supports sentiment and topic extraction across major languages, enabling culturally aware recommendations and reducing language barriers in a multilingual streaming platform. The data collection strategy combines synthetic data generation, publicly available annotated corpora, and ethically sourced user data with rigorous consent and anonymization protocols. The methodology includes a robust evaluation framework comprising offline metrics (precision@k, recall@k, mean reciprocal rank, and normalized discounted cumulative gain) and online A/B testing to assess engagement uplift, session duration, skip rates, and user-reported satisfaction across diverse linguistic groups. A/B experiments compare the multimodal approach against baseline collaborative and content-based models, as well as a purely audio-based emotion-driven recommender, to quantify the incremental value of combining audio emotion signals with NLP-derived user intents. The expected outcomes demonstrate improved relevance and diversity of recommendations, higher discovery of new tracks, and reduced cold-start issues through richer user profiles. The system also provides explainable recommendations by generating concise justifications derived from emotional alignment, mood trajectories, and semantic correlations, thereby enhancing trust and user control. Ethical considerations address potential biases in emotion recognition across cultures, handling of sensitive emotional data, and mitigation strategies to prevent intrusive or intrusive personalization. The study contributes methodological advancements in multimodal fusion for recommender systems, multilingual sentiment analysis in streaming contexts, and scalable, privacy-conscious deployment approaches. Potential applications extend beyond music to dynamic audio-visual content platforms requiring emotionally aware, linguistically inclusive personalization, with implications for artist exposure, user engagement, and monetization strategies in global digital ecosystems.

Project Overview

What This Project Is About

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A practical study to create a music recommendation system that uses signals from how listeners feel (emotions) and simpler text data from song descriptions or user feedback. The system will listen to music to detect mood cues, and read natural language text to understand context, then suggest songs that fit the user’s mood and preferences across multiple languages.

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The Problem It Addresses

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Many music apps suggest songs based only on past listens or rough categories, which can miss how a listener feels in the moment or how language and culture influence musical taste. This project aims to combine emotional signals from audio with language-based understanding to improve relevance and inclusivity for multilingual users.

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Objectives of the Project

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1. Build a simple model to infer mood from audio features (like tempo and energy).
2. Incorporate short text inputs (descriptions, feedback) to refine preferences.
3. Integrate mood and text signals to generate personalized playlists.
4. Evaluate how well the recommendations match user satisfaction across languages.
5. Ensure the system is understandable and easy to adapt for new languages.

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What You Will Do Step by Step

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1. Review existing music recommendation ideas and mood analysis basics.
2. Collect a small, multilingual dataset of songs with mood labels and descriptions.
3. Extract simple audio features that relate to mood (e.g., pace, loudness).
4. Process user text feedback to capture preferences in plain language.
5. Combine audio mood signals with text signals to rank songs.
6. Test the system with volunteers and gather feedback.
7. Analyze results and identify ways to improve accuracy.
8. Document the design and lessons learned for future work.

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Expected Outcome

A functional, easy-to-use prototype that recommends songs based on mood inferred from audio and user text, usable in a multilingual streaming setup, with preliminary user satisfaction results and guidance for future enhancements.