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The estimation of rhythmic properties such as tempo, beat positions or metrical structure are central aspects of Music Information Retrieval (MIR) research. Meter inference algorithms are typically designed to track metrical structure in presence of mild deviations of the feature estimates over time in order to account for performance imprecisions, expressive timing or musical effects such as accelerando...
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,...
Chroma features are a popular tool in musical signal processing and information retrieval tasks, providing a compact representation of the tonal content of a piece of music. A variety of approaches to chroma estimation have been proposed, most of which rely on the summation of related frequency partials. However, frequency partials may be incorrectly assigned due to the log/linear relationship of...
Performance of Non-negative Matrix Factorisation (NMF) can be diminished when the underlying factors consist of elements that overlap in the matrix to be factorised. The use of ℓ0 sparsity may improve NMF, however such approaches are generally limited to Euclidean distance. We have previously proposed a stepwise £0 method for Hellinger distance, leading to improved sparse NMF. We extend sparse Hellinger...
Non-negative Matrix Factorisation (NMF) is a commonly used tool in many musical signal processing tasks, including Automatic Music Transcription (AMT). However unsupervised NMF is seen to be problematic in this context, and harmonically constrained variants of NMF have been proposed. While useful, the harmonic constraints may be constrictive in mixed signals. We have previously observed that recovery...
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...
Non-negative Matrix Factorisation (NMF) is a popular tool in which a ‘parts-based’ representation of a non-negative matrix is sought. NMF tends to produce sparse decompositions. This sparsity is a desirable property in many applications, and Sparse NMF (S-NMF) methods have been proposed to enhance this feature. Typically these enforce sparsity through use of a penalty term, and a ℓ1 norm penalty term...
Musical signals can be thought of as being sparse and structured, with few elements active at a given instant and temporal continuity of active elements observed. Greedy algorithms such as Orthogonal Matching Pursuit (OMP), and structured variants, have previously been proposed for Automatic Music Transcription (AMT), however some problems have been noted. Hence, we propose the use of a backwards...
Automatic Music Transcription (AMT) seeks to understand a musical piece in terms of note activities. Matrix decomposition methods are often used for AMT, seeking to decompose a spectrogram over a dictionary matrix of note-specific template vectors. The performance of these methods can suffer due to the large harmonic overlap found in tonal musical spectra. We propose a row weighting scheme that transforms...
Sparse representations have previously been applied to the automatic music transcription (AMT) problem. Structured sparsity, such as group and molecular sparsity allows the introduction of prior knowledge to sparse representations. Molecular sparsity has previously been proposed for AMT, however the use of greedy group sparsity has not previously been proposed for this problem. We propose a greedy...
Non-negative blind signal decomposition methods are widely used for musical signal processing tasks, such as automatic transcription and source separation. A spectrogram can be decomposed into a dictionary of full spectrum basis atoms and their corresponding time activation vectors using methods such as Non-negative Matrix Factorisation (NMF) and Non-negative K-SVD (NN-K-SVD). These methods are constrained...
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