119 citations · 251 across the 9 of their papers we have counts for
10 papers
Model-Based Deep Learning for Music Information Research
Gael Richard, Vincent Lostanlen, Yi-Hsuan Yang +1
In this article, we investigate the notion of model-based deep learning in the realm of music information research (MIR). Loosely speaking, we refer to the term model-based deep le…
Learning to Solve Inverse Problems for Perceptual Sound Matching
Han Han, Vincent Lostanlen, Mathieu Lagrange
Perceptual sound matching (PSM) aims to find the input parameters to a synthesizer so as to best imitate an audio target. Deep learning for PSM optimizes a neural network to analyz…
Instabilities in Convnets for Raw Audio
Daniel Haider, Vincent Lostanlen, Martin Ehler +1
What makes waveform-based deep learning so hard? Despite numerous attempts at training convolutional neural networks (convnets) for filterbank design, they often fail to outperform…
Helicality: An Isomap-based Measure of Octave Equivalence in Audio Data
Sripathi Sridhar, Vincent Lostanlen
Octave equivalence serves as domain-knowledge in MIR systems, including chromagram, spiral convolutional networks, and harmonic CQT. Prior work has applied the Isomap manifold lear…
SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context
Mark Cartwright, Jason Cramer, Ana Elisa Mendez Mendez +9
We present SONYC-UST-V2, a dataset for urban sound tagging with spatiotemporal information. This dataset is aimed for the development and evaluation of machine listening systems fo…
One or Two Components? The Scattering Transform Answers
Vincent Lostanlen, Alice Cohen-Hadria, Juan Pablo Bello
With the aim of constructing a biologically plausible model of machine listening, we study the representation of a multicomponent stationary signal by a wavelet scattering network.…