Interactive Generative Music System Using Real-Time Audio Feature Extraction and Deep Learning for Personalized Soundtracks

Project Abstract

This project presents an interactive generative music system that leverages real-time audio feature extraction and deep learning to produce personalized soundtracks responsive to user context and preferences. The system integrates a modular pipeline comprising real-time audio input processing, feature extraction, a multimodal representation of user state, and a generative neural network capable of conditional music synthesis. Real-time feature extraction focuses on spectral, temporal, rhythmic, and timbral descriptors, including mel-frequency cepstral coefficients, spectral contrast, beat tracking, onset strength, and dynamic range. These features serve as conditioning signals that guide the generative model, enabling adaptive content that aligns with user mood, tempo tolerance, and listening history. To handle the high dimensionality and low-latency requirements, the architecture employs incremental feature updates, efficient buffering strategies, and a lightweight transformer-based decoder trained with a curriculum that gradually increases musical structure complexity. The conditioning framework supports user inputs through explicit controls (mood sliders, activity labels, preferred genres) and implicit signals (physiological proxies such as heart rate inferred via compatible sensors, device context, and historical listening patterns). A concerted data strategy combines a diverse, ethically sourced corpus of royalty-free music with user-generated interactions to train a conditional generative model capable of producing coherent musical segments with controllable attributes such as genre, harmony, rhythm complexity, luminance of timbre, and dynamic loudness. The learning objective optimizes musicality, continuity, and stylistic alignment with the conditioning signals while maintaining responsiveness to novel prompts. The system also implements a preference-aware recommender module that steers the generation toward user-specific playlists, while preserving exploratory generation to broaden musical horizons. Evaluation comprises quantitative metrics for temporal coherence, tonal stability, and perceptual similarity to target styles, complemented by user studies assessing engagement, satisfaction, and perceived personalization. A/B comparisons with baseline rule-based and non-conditioned generative systems quantify improvements in adaptability, expressiveness, and user agency. The results demonstrate that real-time feature conditioning significantly enhances listener immersion, enabling the produced soundtracks to dynamically reflect changes in mood, activity, and context without perceptible latency. The platform supports seamless integration with popular digital audio workstations and streaming environments, enabling composers and developers to prototype interactive scoring, adaptive soundtracks for games, and personalized ambience for multimedia installations. Ethical considerations address data privacy, consent for collected physiological signals, and transparency in model-generated content to prevent stylistic misattribution. The project contributes a scalable architecture for real-time, user-centric music generation and a robust evaluation framework for measuring personalization quality in interactive audio systems, offering practical implications for music technology, human–computer interaction, and computational creativity.

Project Overview

What This Project Is About

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A hands-on exploration of how computer systems can create personalized music on the fly. The project combines real-time analysis of audio features with simple deep-learning models to generate evolving soundtracks tailored to an individual's mood, tempo, or context. It focuses on making music that reacts to input from a user or environment rather than pre-recorded tracks alone.

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The Problem It Addresses

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Many listening experiences are generic and not suited to each moment or user. There is a need for systems that can adapt music in real time, while still sounding musical and coherent. This project targets easier-to-use, explainable methods that can be understood by non-experts and implemented within university-level coursework.

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Objectives of the Project

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1. Understand real-time audio feature extraction and the basics of neural networks.
2. Build a simple interactive system that generates music in response to input signals.
3. Evaluate how well the generated music matches user preferences or states.
4. Provide a playable prototype and a short user guide.

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What You Will Do Step by Step

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1) Learn core concepts of audio features and basic model design. 2) Collect or simulate input data (like tempo suggestions or mood cues). 3) Implement real-time feature extraction from audio signals. 4) Train or fine-tune a lightweight generative model. 5) Create an interface to control or trigger generation. 6) Test with users and gather feedback. 7) Refine the system for smoother playback. 8) Document results and prepare a demonstration.

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Expected Outcome

A functioning prototype that generates short custom soundtracks in real time, along with a report describing its design, performance, and potential improvements. The project aims to produce music that adapts to user input while remaining cohesive and listenable.