859 citations · 1.1k across the 7 of their papers we have counts for
8 papers · 1 filter
Explore This! Beat and Downbeat Tracking From a Learned Tatum Grid
Robert Kihlborg, André Holzapfel, Jan Schlüter
State-of-the-art beat tracking is based on neural networks that model beats and downbeats as individual events, often followed by a separate Dynamic Bayesian Network (DBN) enforcin…
Music Boomerang: Reusing Diffusion Models for Data Augmentation and Audio Manipulation
Alexander Fichtinger, Jan Schlüter, Gerhard Widmer
Generative models of music audio are typically used to generate output based solely on a text prompt or melody. Boomerang sampling, recently proposed for the image domain, allows g…
Learning General Audio Representations with Large-Scale Training of Patchout Audio Transformers
Khaled Koutini, Shahed Masoudian, Florian Schmid +3
The success of supervised deep learning methods is largely due to their ability to learn relevant features from raw data. Deep Neural Networks (DNNs) trained on large-scale dataset…
Musika! Fast Infinite Waveform Music Generation
Marco Pasini, Jan Schlüter
Fast and user-controllable music generation could enable novel ways of composing or performing music. However, state-of-the-art music generation systems require large amounts of da…
EfficientLEAF: A Faster LEarnable Audio Frontend of Questionable Use
Jan Schlüter, Gerald Gutenbrunner
In audio classification, differentiable auditory filterbanks with few parameters cover the middle ground between hard-coded spectrograms and raw audio. LEAF (arXiv:2101.08596), a G…
Efficient Training of Audio Transformers with Patchout
Khaled Koutini, Jan Schlüter, Hamid Eghbal-zadeh +1
The great success of transformer-based models in natural language processing (NLP) has led to various attempts at adapting these architectures to other domains such as vision and a…
Deep Learning for Audio Signal Processing
Hendrik Purwins, Bo Li, Tuomas Virtanen +3
Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music,…
madmom: a new Python Audio and Music Signal Processing Library
Sebastian Böck, Filip Korzeniowski, Jan Schlüter +2
In this paper, we present madmom, an open-source audio processing and music information retrieval (MIR) library written in Python. madmom features a concise, NumPy-compatible, obje…