Implementing a Sound Identifier in Python

Explore the implementation of a Shazam-style sound recognizer in Python during this 22-minute conference talk from EuroPython 2016. Dive into Digital Signal Processing (DSP) techniques and powerful libraries used to create an audio identification system. Learn about the project's structure, including a classifier for fingerprinting and storing audio, and a recognizer for matching smaller audio chunks. Discover the journey from concept to implementation, addressing challenges and future improvements. Gain insights into music information retrieval, normalization, fingerprinting, spectrograms, nearest neighbor algorithms, and hash storage. No prior DSP knowledge required – only Python experience needed to follow along. Access the project's code on GitHub and understand how it was inspired by a FOSDEM 2016 talk on over-the-air audio identification.

Introduction
Music Information Retrieval
Why Python
Demo
Normalizer
Fingerprint
Diagram
Spectrogram
Nearest Neighbor
Anchor Points
Hash
Storage
Deja Vu
Shazam
Genius
Notebook
MusicBrainz