• Publications
  • Influence
A tutorial on onset detection in music signals
TLDR
Methods based on the use of explicitly predefined signal features: the signal's amplitude envelope, spectral magnitudes and phases, time-frequency representations, and methods based on probabilistic signal models are discussed.
Automatic Tagging Using Deep Convolutional Neural Networks
TLDR
The experiments show that mel-spectrogram is an effective time-frequency representation for automatic tagging and that more complex models benefit from more training data.
The Music Ontology
TLDR
The Music Ontology is described: a formal framework for dealing with music-related information on the Semantic Web, including editorial, cultural and acoustic information, and how this ontology can act as a grounding for more domain-specific knowledge representation.
Structural Segmentation of Musical Audio by Constrained Clustering
  • M. Levy, M. Sandler
  • Computer Science
    IEEE Transactions on Audio, Speech, and Language…
  • 1 February 2008
TLDR
Experimental results show that in many cases the resulting segmentations correspond well to conventional notions of musical form, and how the constrained clustering approach can be extended to include prior musical knowledge, input from other machine approaches, or semi-supervision.
Sonic visualiser: an open source application for viewing, analysing, and annotating music audio files
TLDR
Sonic Visualiser is a friendly and flexible end-user desktop application for analysis, visualisation, and annotation of music audio files that has a user interface that resembles familiar audio editing applications, a set of useful standard visualisation facilities, and support for a plugin format for additional automated analysis methods.
Transfer Learning for Music Classification and Regression Tasks
TLDR
This paper proposes to use a pre-trained convnet feature, a concatenated feature vector using the activations of feature maps of multiple layers in a trained convolutional network, and shows how it can serve as general-purpose music representation.
Convolutional recurrent neural networks for music classification
TLDR
It is found that CRNN show a strong performance with respect to the number of parameter and training time, indicating the effectiveness of its hybrid structure in music feature extraction and feature summarisation.
Detecting harmonic change in musical audio
TLDR
Initial experiments show that the algorithm can successfully detect harmonic changes such as chord boundaries in polyphonic audio recordings.
Symbolic Representation of Musical Chords: A Proposed Syntax for Text Annotations
TLDR
This paper defines a rigid, contextindependent syntax for representing chord symbols in text, supported with a new database of annotations using this system, and proposes a text represention for musical chord symbols that is simple and intuitive for musically trained individuals to write and understand.
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