6 papers
CNN Models for Microphone Array Covariance Matrix Upsampling and Acoustic Imaging
Marianthi Adamopoulou, Parthasaarathy Sudarsanam, David Diaz-Guerra +5
Acoustic imaging visualization is a core methodology in acoustics, enabling spatial analysis of sound sources and acoustic scenes. However, limited sensor availability in practical…
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…
The Spheres Dataset: Multitrack Orchestral Recordings for Music Source Separation and Information Retrieval
Jaime Garcia-Martinez, David Diaz-Guerra, John Anderson +5
This paper introduces The Spheres dataset, multitrack orchestral recordings designed to advance machine learning research in music source separation and related MIR tasks within th…
Stereo Sound Event Localization and Detection with Onscreen/offscreen Classification
Kazuki Shimada, Archontis Politis, Iran R. Roman +10
This paper presents the objective, dataset, baseline, and metrics of Task 3 of the DCASE2025 Challenge on sound event localization and detection (SELD). In previous editions, the c…
Score-informed Music Source Separation: Improving Synthetic-to-real Generalization in Classical Music
Eetu Tunturi, David Diaz-Guerra, Archontis Politis +1
Music source separation is the task of separating a mixture of instruments into constituent tracks. Music source separation models are typically trained using only audio data, alth…
SynthSOD: Developing an Heterogeneous Dataset for Orchestra Music Source Separation
Jaime Garcia-Martinez, David Diaz-Guerra, Archontis Politis +3
Recent advancements in music source separation have significantly progressed, particularly in isolating vocals, drums, and bass elements from mixed tracks. These developments owe m…