Denise Azucena

About Me

Denise is an audio engineer with over 5 years of experience in adverts, short films, and audiobooks. She holds an MSc in Sound and Music Computing from Queen Mary University of London, where she focused on AI and data science. She now works as a freelance software developer focused on machine learning, with a strong interest in audio programming and sound design.

Combining her deep domain knowledge of sound with machine learning and audio information retrieval, she draws on her post-production experience to build software tools that address real pain points in the industry.

Sessions

  • Cataloguing the Chaos

    An Embedded-Based Auto-Tagging System for SFX Libraries With FMOD Integration
    15:20 - 15:40 UTC | Friday 16th October 2026 | ADCx Gather
    Beginner
    Intermediate

    Managing third-party SFX libraries is one of the most time-consuming tasks in game audio production. Sound designers and audio programmers alike face a number of challenges due to inconsistent folder structures, missing metadata, and unusual naming conventions. Current middleware frameworks such as FMOD and Wwise provide no automated solution, which leaves organisation entirely to the developer and/or sound designer. This talk presents a SFX cataloguing system using pretrained audio embeddings, a fine-tuned classifier, and a Retrieval-Augmented Generation (RAG) pipeline to automatically tag, and structure SFX libraries. By deriving tags directly from audio features and related data, this project can organise […]

  • Build Your Own Shazam

    Audio Fingerprinting from First Principles
    10:00 - 13:00 UTC | Monday 9th November 2026 | Empire
    In-Person Only

    Audio fingerprinting is one of the most successful real-world applications of audio signal processing. It powers music recognition systems such as Shazam, enabling reliable identification of songs from short, noisy recordings in a few seconds. While many audio developers are familiar with audio fingerprinting in practice, the underlying techniques are often encountered only at a high level. In this hands-on workshop, participants will build a complete audio fingerprinting engine based on Avery Wang’s landmark 2003 Shazam paper. Starting with raw audio, we will progressively construct the identification pipeline: generating spectrograms, detecting robust spectral landmarks, creating constellation maps, producing landmark hashes, […]