66 citations · 150 across the 9 of their papers we have counts for
9 papers
TiRex-2: Generalizing TiRex to Multivariate Data and Streaming
Patrick Podest, Marco Pichler, Elias Bürger +7
We introduce TiRex-2, a recurrent xLSTM-based time series foundation model that generalizes the univariate TiRex to multivariate forecasting with both past and future covariates. R…
On Subquadratic Architectures: From Applications to Principles
Anamaria-Roberta Hartl, Levente Zólyomi, David Stap +6
Transformers dominate modern sequence modeling, but their quadratic attention incurs substantial computational cost. Subquadratic architectures offer a scalable alternative. Howeve…
xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference
Maximilian Beck, Korbinian Pöppel, Phillip Lippe +5
Recent breakthroughs in solving reasoning, math and coding problems with Large Language Models (LLMs) have been enabled by investing substantial computation budgets at inference ti…
Deep Learning for MIR Tutorial
Alexander Schindler, Thomas Lidy, Sebastian Böck
Deep Learning has become state of the art in visual computing and continuously emerges into the Music Information Retrieval (MIR) and audio retrieval domain. In order to bring atte…
Deep Polyphonic ADSR Piano Note Transcription
Rainer Kelz, Sebastian Böck, Gerhard Widmer
We investigate a late-fusion approach to piano transcription, combined with a strong temporal prior in the form of a handcrafted Hidden Markov Model (HMM). The network architecture…
Multitask Learning for Polyphonic Piano Transcription, a Case Study
Rainer Kelz, Sebastian Böck, Gerhard Widmer
Viewing polyphonic piano transcription as a multitask learning problem, where we need to simultaneously predict onsets, intermediate frames and offsets of notes, we investigate the…
On the Potential of Simple Framewise Approaches to Piano Transcription
Rainer Kelz, Matthias Dorfer, Filip Korzeniowski +3
In an attempt at exploring the limitations of simple approaches to the task of piano transcription (as usually defined in MIR), we conduct an in-depth analysis of neural network-ba…
Live Score Following on Sheet Music Images
Matthias Dorfer, Andreas Arzt, Sebastian Böck +2
In this demo we show a novel approach to score following. Instead of relying on some symbolic representation, we are using a multi-modal convolutional neural network to match the i…
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…