An iterative hard thresholding approach to ℓ&lt;inf&gt;0&lt;/inf&gt; sparse Hellinger NMF

O'Hanlon, Ken; Sandler, Mark

doi:10.1109/icassp.2016.7472576

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…PED is related to β-divergence through membership of the αβ divergence family [17], with similar error function scaling e.g. linear scaling is found for β = 2η = 1 [18]. The experiments described in [12] were on a database of MIDI chords, with improved performance observed for PED relative to βdivergence, particularly as η → 0.…”

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confidence: 75%

Improved template based chord recognition using the CRP feature

O'Hanlon

Ewert

Pauwels

et al. 2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The task of chord recognition in music signals is often based upon pattern matching in chromagrams. Many variants of chroma exist and quality of chord recognition is related to the feature employed. Chroma Reduced Pitch (CRP) features are interesting in this context as they were designed to improve timbre invariance for the purpose of query retrieval. Their reapplication to chord recognition, however, has not been successful in previous studies. We consider that the default parametrisation of CRP attenuates some tonal information, as well as timbral, and consider alternatives to this default. We also provide a variant of a recently proposed compositional chroma feature, adapted for music pieces, rather than one instrument. Experiments described show improved results compared to existing features.

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confidence: 75%

Improved template based chord recognition using the CRP feature

O'Hanlon

Ewert

Pauwels

et al. 2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Bertin et al [33] propose a tempering scheme favoring NMF with Itakura-Saito divergence to global minima. O'Hanlon and Sandler [34] propose an iterative hard thresholding approach for l 0 sparse NMF problem with Hellinger distance. ERBT spectrograms of polyphonic music pieces are decomposed directly and a pitch salience matrix is calculate to detect active notes.…”

Section: Related Workmentioning

confidence: 99%

Piano multipitch estimation using sparse coding embedded deep learning

Guan

et al. 2018

J AUDIO SPEECH MUSIC PROC.

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As the foundation of many applications, multipitch estimation problem has always been the focus of acoustic music processing; however, existing algorithms perform deficiently due to its complexity. In this paper, we employ deep learning to address piano multipitch estimation problem by proposing MPENet based on a novel multimodal sparse incoherent non-negative matrix factorization (NMF) layer. This layer originates from a multimodal NMF problem with Lorentzian-BlockFrobenius sparsity constraint and incoherentness regularization. Experiments show that MPENet achieves state-of-the-art performance (83.65% F-measure for polyphony level 6) on RAND subset of MAPS dataset. MPENet enables NMF to do online learning and accomplishes multi-label classification by using only monophonic samples as training data. In addition, our layer algorithms can be easily modified and redeveloped for a wide variety of problems.

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“…Several important instances of Csiszár f -divergence have been used in NMF, including the 1 distance [37] and the family of α-divergences [3], [4]. In particular, the α-divergences include Hellinger distance (α = 1/2) [38], [39] and the (generalized) Kullback-Leibler (KL) divergence (α → 1) [40], [41]. Moreover, for these special cases, d…”

Section: B Overview Of Divergencesmentioning

confidence: 99%

Online Nonnegative Matrix Factorization With Outliers

Zhao

Tan

2017

IEEE Trans. Signal Process.

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Abstract-We propose a unified and systematic framework for performing online nonnegative matrix factorization in the presence of outliers. Our framework is particularly suited to large-scale data. We propose two solvers based on projected gradient descent and the alternating direction method of multipliers. We prove that the sequence of objective values converges almost surely by appealing to the quasi-martingale convergence theorem. We also show the sequence of learned dictionaries converges to the set of stationary points of the expected loss function almost surely. In addition, we extend our basic problem formulation to various settings with different constraints and regularizers. We also adapt the solvers and analyses to each setting. We perform extensive experiments on both synthetic and real datasets. These experiments demonstrate the computational efficiency and efficacy of our algorithms on tasks such as (partsbased) basis learning, image denoising, shadow removal and foreground-background separation.

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An iterative hard thresholding approach to ℓ<inf>0</inf> sparse Hellinger NMF

Cited by 5 publications

References 24 publications

Improved template based chord recognition using the CRP feature

Improved template based chord recognition using the CRP feature

Piano multipitch estimation using sparse coding embedded deep learning

Online Nonnegative Matrix Factorization With Outliers

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