320 research outputs found
AUTOMATIC MUSIC TRANSCRIPTION USING ROW WEIGHTED DECOMPOSITIONS
(c) 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Published in: Proc IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), Vancouver, Canada, 26-31 May 2013. pp. 16-20
POLYPHONIC PIANO TRANSCRIPTION USING NON-NEGATIVE MATRIX FACTORISATION WITH GROUP SPARSITY
(c)2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Published in: Proc IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), Florence, Italy, 5-9 May 2014. pp.3136-3140
Polyphonic piano transcription using non-negative Matrix Factorisation with group sparsity
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 state-of-the-art NMF-based AMT system
Non-Negative Group Sparsity with Subspace Note Modelling for Polyphonic Transcription
This work was supported by EPSRC Platform Grant EPSRC EP/K009559/1, EPSRC Grant EP/L027119/1, and EPSRC Grant EP/J010375/1
Automatic Music Transcription using Structure and Sparsity
PhdAutomatic Music Transcription seeks a machine understanding of a musical signal in terms of
pitch-time activations. One popular approach to this problem is the use of spectrogram decompositions,
whereby a signal matrix is decomposed over a dictionary of spectral templates, each
representing a note. Typically the decomposition is performed using gradient descent based
methods, performed using multiplicative updates based on Non-negative Matrix Factorisation
(NMF). The final representation may be expected to be sparse, as the musical signal itself is considered
to consist of few active notes. In this thesis some concepts that are familiar in the sparse
representations literature are introduced to the AMT problem. Structured sparsity assumes that
certain atoms tend to be active together. In the context of AMT this affords the use of subspace
modelling of notes, and non-negative group sparse algorithms are proposed in order to exploit
the greater modelling capability introduced. Stepwise methods are often used for decomposing
sparse signals and their use for AMT has previously been limited. Some new approaches to
AMT are proposed by incorporation of stepwise optimal approaches with promising results seen.
Dictionary coherence is used to provide recovery conditions for sparse algorithms. While such
guarantees are not possible in the context of AMT, it is found that coherence is a useful parameter
to consider, affording improved performance in spectrogram decompositions
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