6 papers
Cover First, Disagree Softly: Rethinking Mismatch-First Active Learning for Frame-Level Audio Classification
Shiqi Zhang, Tuomas Virtanen
Sound event detection relies on frame-level strong labels whose annotation is expensive. Active learning addresses this problem by selecting the audio segments whose labels help th…
Greedy Volume Maximization of Gradient Embeddings for Long-Tailed Frame-Level Bioacoustic Active Learning
Shiqi Zhang, Marius Faiß, Ariana Strandburg-Peshkin +1
Bioacoustic call-type classification relies on costly expert annotation. Active learning can reduce this burden by selecting a small batch of segments for expert annotation and usi…
Mixture-Constrained Max Pooling Improves Separation-Based Bird Species Classification
Yuzhu Wang, Kalle Lahtinen, Patrik Lauha +4
Bird species classification from field recordings remains challenging due to overlapping vocalizations and incomplete species labels. We study source separation as a preprocessing…
Learning Input-Channel Permutation Equivariance for Multi-Channel Source Separation: Reducing Bleeding in Small Music Ensembles
Ruchi Pandey, Jaime Garcia-Martinez, Pablo Cabanas-Molero +5
Microphone bleed is a persistent challenge in small ensembles and orchestral recordings, where close microphones intended for individual instruments also capture leakage from nearb…
Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event Detection
Shiqi Zhang, Tuomas Virtanen
Bioacoustic sound event detection (BioSED) is crucial for biodiversity conservation but faces practical challenges during model development and training: limited amounts of annotat…
Timing and Dynamics of the Rosanna Shuffle
Esa Räsänen, Niko Gullsten, Otto Pulkkinen +1
The Rosanna shuffle, the drum pattern from Toto's 1982 hit "Rosanna", is one of the most recognized drum beats in popular music. Recorded by Jeff Porcaro, this drum beat features a…