46 citations · 57 across the 7 of their papers we have counts for
6 papers · 1 filter
Towards Interpretable Polyphonic Transcription with Invertible Neural Networks
Rainer Kelz, Gerhard Widmer
We explore a novel way of conceptualising the task of polyphonic music transcription, using so-called invertible neural networks. Invertible models unify both discriminative and ge…
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…
Learning to Transcribe by Ear
Rainer Kelz, Gerhard Widmer
Rethinking how to model polyphonic transcription formally, we frame it as a reinforcement learning task. Such a task formulation encompasses the notion of a musical agent and an en…
Investigating Label Noise Sensitivity of Convolutional Neural Networks for Fine Grained Audio Signal Labelling
Rainer Kelz, Gerhard Widmer
We measure the effect of small amounts of systematic and random label noise caused by slightly misaligned ground truth labels in a fine grained audio signal labeling task. The task…
An Experimental Analysis of the Entanglement Problem in Neural-Network-based Music Transcription Systems
Rainer Kelz, Gerhard Widmer
Several recent polyphonic music transcription systems have utilized deep neural networks to achieve state of the art results on various benchmark datasets, pushing the envelope on…