• Corpus ID: 15972862

Musical Key Extraction from Audio Using Profile Training

@inproceedings{Par2006MusicalKE,
  title={Musical Key Extraction from Audio Using Profile Training},
  author={Steven van de Par and Martin F. McKinney and Andr{\'e} Redert},
  booktitle={ISMIR},
  year={2006}
}
A new method is presented for extracting the musical key from raw audio data. The method is based on the extraction of chromagrams using a new approach for tonal component selection taking into account auditory masking. The extracted chromagrams were used to train three key profiles for major and three key profiles for minor keys. The three trained key profiles differ in their temporal weighting of information across the duration of the song. One profile is based on uniform weighting while the… 
ismir2006.ismir.net
31 Citations
Highly Influential Citations
3
Background Citations
11
Methods Citations
13
Results Citations
1

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31 Citations

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A probabilistic framework that combines two closely related musical tasks—chord recognition and key extraction from audio—and achieves state-of-the-art performance in both applications is presented, and a solution to a bottleneck problem in machine learning approaches is suggested.
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A method that can be used to effectively identify cover songs is described and the beat/chroma-synchronous chromagram, which is insensitive to the variation of the timber or rhythm of songs but sensitive to the melody, is chosen.
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Musical Key Estimation with Unsupervised Pattern Recognition
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  • Computer Science
    2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC)
  • 2019
TLDR
This work employs an unsupervised minimization technique to determine both the mode and tonic center of a piece of music in contrast to previous methods that employ supervised learning techniques such as multiple classification models, support vector machines (SVMs), hidden Markov models, multinomial linear regressions, artificial neural networks, convolutional neural networks (CNNs), and other high-complexity machine learning algorithms.
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TLDR
A method for estimating the local keys of an audio signal by investigating the possible combination and extension of different previous proposed global key estimation approaches and introducing key dependency on the harmonic and the metric structures.
Perceptually motivated quasi-periodic signal selection for polyphonic music transcription
  • M. Triki, D. Slock
  • Computer Science
    2009 IEEE International Conference on Acoustics, Speech and Signal Processing
  • 2009
TLDR
This work proposes a physically-motivated audio signal analysis followed by an auditory-based selection that allows for a better time/frequency resolution tradeoff, while the auditory distance discards the redundant/non-relevant information.
Joint Estimation of Musical Content Information From an Audio Signal. (Estimation conjointe d'information de contenu musical d'un signal audio)
TLDR
A model that allows the joint estimation of the chords, the keys and the downbeats from polyphonic music recordings is developed and it is intended to show that integrating knowledge of mutual dependencies between several descriptors of musical content improves their estimation.
Cross recurrence quantification for cover song identification
TLDR
A recurrence quantification analysis measure is proposed that allows the tracking of potentially curved and disrupted traces in cross recurrence plots (CRPs) and it is shown that this method identifies cover songs with a higher accuracy as compared to previously published techniques.
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