• Publications
  • Influence
Learning in the presence of concept drift and hidden contexts
TLDR
We describe a family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear. Expand
  • 581
  • 65
  • PDF
Learning in the Presence of Concept Drift and Hidden Contexts
TLDR
We describe a family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear. Expand
  • 985
  • 53
Incremental Reduced Error Pruning
TLDR
We propose Incremental Reduced Error Pruning — a method that integrates pre- and postpruning — as an alternative solution. Expand
  • 426
  • 32
Deep Linear Discriminant Analysis
TLDR
We introduce Deep Linear Discriminant Analysis (DeepLDA) which learns linearly separable latent representations in an end-to-end fashion. Expand
  • 74
  • 21
  • PDF
Evaluating Rhythmic descriptors for Musical Genre Classification
TLDR
We consider a set of rhythmic descriptors for which we provide procedures of automatic extraction from audio signals. Expand
  • 136
  • 19
  • PDF
Improvements of Audio-Based Music Similarity and Genre Classificaton
TLDR
We combine spectral similarity with complementary information from fluctuation patterns including two new descriptors derived from them. Expand
  • 266
  • 15
  • PDF
Tracking Context Changes through Meta-Learning
  • G. Widmer
  • Computer Science
  • Machine Learning
  • 1 June 1997
TLDR
The article deals with the problem of learning incrementally (‘on-line’) in domains where the target concepts are context-dependent, so that changes in context can produce more or less radical changes in the associated concepts. Expand
  • 128
  • 14
CP-JKU SUBMISSIONS FOR DCASE-2016 : A HYBRID APPROACH USING BINAURAL I-VECTORS AND DEEP CONVOLUTIONAL NEURAL NETWORKS
This report describes the 4 submissions for Task 1 (Audio scene classification) of the DCASE-2016 challenge of the CP-JKU team. We propose 4 different approaches for Audio Scene Classification (ASC).Expand
  • 132
  • 14
  • PDF
On the Potential of Simple Framewise Approaches to Piano Transcription
TLDR
In an attempt at exploring the limitations of simple approaches to the task of piano transcription (as usually defined in MIR), we show that it is possible, by simple bottom-up frame-wise processing, to obtain a piano transcriber that outperforms the current published state of the art on the publicly available MAPS dataset. Expand
  • 81
  • 13
  • PDF
madmom: A New Python Audio and Music Signal Processing Library
TLDR
We present madmom, an open-source audio processing and music information retrieval (MIR) library written in Python. Expand
  • 123
  • 12
  • PDF
...
1
2
3
4
5
...