Lab

Experiments & Prototypes in
Human-AI Co-Creation & Musical Intelligence.

Posts

JAN 21 2026

Learning Normal Patterns in Musical Loops

An unsupervised framework for analyzing audio patterns in musical loops through anomaly detection. Combines HTS-AT feature extraction with Deep SVDD to learn normal patterns without labeled training data, addressing variable-length audio challenges.

Building Tools for Hierarchical Self-Organization

Two complementary Python repositories for training and analyzing Growing Hierarchical Self-Organizing Maps, focusing on design decisions, trade-offs, and practical research workflows.

MIDI Loop Extraction for Symbolic Music Datasets

A Python package for extracting instrument-specific musical loops from MIDI datasets. Implements a modular pipeline for bass, guitar, piano, drums, and synthesizer parts with configurable filtering and multiple output formats.