Denise Azucena

Software Developer - Machine Learning

Independent

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
    15:20 - 15:40 UTC | Friday 16th October 2026 | ADCx Gather
    Beginner
    Intermediate
    Advanced

    Managing third-party SFX libraries is one of post-production's most time-consuming tasks. Sound designers and audio programmers routinely struggle with inconsistent vendor folder structures, missing metadata, and arbitrary naming conventions. Existing systems suffer from two main gaps: they rarely match up how different vendors name things into a single taxonomy, and their all-or-nothing automation either collect hidden metadata debt or manual review for every track by the user.  The project handles both cases. If a file already has some metadata, even messy or inconsistent, it gets cleaned up and mapped onto the UCS standard. If it has none at all, an […]

  • Build Your Own Shazam

    Audio Fingerprinting from First Principles
    10:00 - 13:00 UTC | Monday 9th November 2026 | Empire
    Beginner
    Intermediate
    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, […]