Structured sparsity for automatic music transcription

O'Hanlon, Ken; Nagano, Hidehisa; Plumbley, Mark D.

doi:10.1109/icassp.2012.6287911

Cited by 15 publications

(21 citation statements)

References 12 publications

(26 reference statements)

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“…We previously proposed the Non-Negative Nearest Subspace OMP (NN-NS-OMP) [15], using the selection criteria:…”

Section: Non-negative Group Sparse Methodsmentioning

confidence: 99%

“…We propose to use group sparsity for this purpose, and develop a suite of non-negative group sparse algorithms. OMP-based methods are first proposed [15], but noted problems with this class of approaches in this context [9] [11] lead us to propose an alternative stepwise method, employing backwards elimination [16]. We previously proposed these approaches in [15] [16], and offer a direct comparison here alongside a further comparison of subspace and datapoint modelling [9].…”

Section: A Contributions Of This Papermentioning

confidence: 99%

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Non-Negative Group Sparsity with Subspace Note Modelling for Polyphonic Transcription

O'Hanlon

Nagano

Keriven

et al. 2016

IEEE/ACM Trans. Audio Speech Lang. Process.

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Abstract-Automatic Music Transcription (AMT) can be performed by deriving a pitch-time representation through decomposition of a spectrogram with a dictionary of pitch-labelled atoms. Typically, Non-negative Matrix Factorisation (NMF) methods are used to decompose magnitude spectrograms. One atom is often used to represent each note. However, the spectrum of a note may change over time. Previous research considered this variability using different atoms to model specific parts of a note, or large dictionaries comprised of datapoints from the spectrograms of full notes. In this paper the use of subspace modelling of note spectra is explored, with group sparsity employed as a means of coupling activations of related atoms into a pitched subspace.Stepwise and gradient-based methods for non-negative group sparse decompositions are proposed. Finally, a group sparse NMF approach is used to tune a generic harmonic subspace dictionary, leading to improved NMF-based AMT results.

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“…We previously proposed the Non-Negative Nearest Subspace OMP (NN-NS-OMP) [15], using the selection criteria:…”

Section: Non-negative Group Sparse Methodsmentioning

confidence: 99%

Section: A Contributions Of This Papermentioning

confidence: 99%

Non-Negative Group Sparsity with Subspace Note Modelling for Polyphonic Transcription

O'Hanlon

Nagano

Keriven

et al. 2016

IEEE/ACM Trans. Audio Speech Lang. Process.

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View full text Add to dashboard Cite

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“…The use of group sparsity has previously been proposed by the authors for the purpose of AMT using stepwise NMD methods [7] [8] and shown to afford improved AMT. However, this previous work used dictionaries that were learnt offline, and it is intended to construct a method that is more adaptable to the signal, and affords the use of group sparsity.…”

Section: Methodsmentioning

confidence: 99%

“…Previously, we have explored the use of group sparsity for AMT using stepwise approaches, based on greedy pursuits [7], and backwards elimination [8], observing that the use of a group of atoms to represent a note affords improved AMT. However, these approaches used fixed dictionaries learnt offline that may not be adaptable to other signals.…”

Section: Introductionmentioning

confidence: 99%

Polyphonic piano transcription using non-negative Matrix Factorisation with group sparsity

O'Hanlon

Plumbley

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Non-negative Matrix Factorisation (NMF) is a popular tool in musical signal processing. However, problems using this methodology in the context of Automatic Music Transcription (AMT) have been noted resulting in the proposal of supervised and constrained variants of NMF for this purpose. Group sparsity has previously been seen to be effective for AMT when used with stepwise methods. In this paper group sparsity is introduced to supervised NMF decompositions and a dictionary tuning approach to AMT is proposed based upon group sparse NMF using the β-divergence. Experimental results are given showing improved AMT results over the stateof-the-art NMF-based AMT system

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“…Bertin et al [15] employed the non-negative k-means singular value decomposition algorithm (NKSVD) algorithm for multi-pitch detection, comparing its performance with the NMF algorithm. More recently in [97], structured sparsity (also called group sparsity) was applied to piano transcription. In group sparsity, groups of atoms tend to be active at the same time.…”

Section: Spectrogram Factorisation-based Multi-pitch Detectionmentioning

confidence: 99%

Automatic music transcription: challenges and future directions

et al. 2013

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This is the unspecified version of the paper.This version of the publication may differ from the final published version. Abstract Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects. Permanent repository link

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Structured sparsity for automatic music transcription

Cited by 15 publications

References 12 publications

Non-Negative Group Sparsity with Subspace Note Modelling for Polyphonic Transcription

Non-Negative Group Sparsity with Subspace Note Modelling for Polyphonic Transcription

Polyphonic piano transcription using non-negative Matrix Factorisation with group sparsity

Automatic music transcription: challenges and future directions

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