Denise Azucena

Software Developer - Machine Learning

Independent

About Me

Denise is an audio engineer turned machine learning 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. Her area of interests include audio similarity search, ranking and recommendation systems, and intelligent audio production.

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

  • Build Your Own Shazam

    Audio Fingerprinting from First Principles
    14:00 - 17:00 UTC | Monday 9th November 2026 | SS Great Britain
    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, […]