Convex non-negative matrix factorization for automatic music structure identification

Nieto, Oriol; Jehan, Tristan

doi:10.1109/icassp.2013.6637644

Cited by 23 publications

(19 citation statements)

References 10 publications

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“…There are principally two trends for the detection of homogeneous segments. The first one consists in locating zones of specific texture within similarity matrices using image filtering or matrix decomposition methods [35], [22], [36]. The second approach aims at detecting homogeneous portions of the sequence of feature vectors using multi-level classification processes, relying on variations of clustering, or on Hidden Markov Models (HMM) [3], [37], [38].…”

Section: B Segment Detection Criteriamentioning

confidence: 99%

Estimating the Structural Segmentation of Popular Music Pieces Under Regularity Constraints

Sargent

Bimbot

2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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Music structure estimation has recently emerged as a central topic within the field of Music Information Retrieval. Indeed, as music is a highly structured information stream, knowledge of how a music piece is organized represents a key challenge to enhance the management and exploitation of large music collections. This article focuses on the benefits that can be expected from a regularity constraint on the structural segmentation of popular music pieces. Specifically here, we study how a constraint which favors structural segments of comparable size provides a better conditioning of the boundary estimation process. Firstly, we propose a formulation of the structural segmentation task as an optimization process which separates the contribution from the audio features and the one from the constraint. We illustrate how the corresponding cost function can be minimized using a Viterbi algorithm. We present briefly its implementation and results in three systems designed for and submitted to the MIREX 2010, 2011 and 2012 evaluation campaigns. Then, we explore the benefits of the regularity constraint as an efficient mean for combining the outputs of a selection of systems presented at MIREX between 2010 and 2015, yielding a level of performance competitive to that of the state-of-the-art on the "MIREX10" dataset (100 J-Pop songs from the RWC database).

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Section: B Segment Detection Criteriamentioning

confidence: 99%

Estimating the Structural Segmentation of Popular Music Pieces Under Regularity Constraints

Sargent

Bimbot

2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…To analyze the performance of algorithms on our folk music dataset, we gathered several publicly available implementation scenarios of segmentation algorithms: Segmentino [27], MSAF-Foote [4], MSAF-SCluster [16], MSAF-SF [13], MSAF-CNMF3 [11], and MSAF-SI-PLCA [28]. All except Segmentino are available within the Music Structure Analysis Framework (MSAF) and they were tested with three different feature types: MFCCs, HPCP chromagrams, and tonal centroid features, Tonnetz.…”

Section: Evaluating the State Of The Artmentioning

confidence: 99%

“…In [9], the authors use NMF to search for acoustically similar frames in selfsimilarity matrices, while in [10] the authors use NMF for searching repeating patterns in beat-synchronous chromagrams. A novel adapted matrix factorization technique was presented by the authors in [11], who use convex constraints in the factorization process to decompose the similarity matrix in a way that individual centroids can be interpreted as different sections of musical piece. A graph based musical structure analysis method is presented in [12], where a graph is constructed from a sparse representation of feature vectors.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Segmentation of Folk Music Recordings

Bohak

Marolt

2016

Mathematical Problems in Engineering

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The paper presents a novel method for automatic segmentation of folk music field recordings. The method is based on a distance measure that uses dynamic time warping to cope with tempo variations and a dynamic programming approach to handle pitch drifting for finding similarities and estimating the length of repeating segment. A probabilistic framework based on HMM is used to find segment boundaries, searching for optimal match between the expected segment length, between-segment similarities, and likely locations of segment beginnings. Evaluation of several current state-of-the-art approaches for segmentation of commercial music is presented and their weaknesses when dealing with folk music are exposed, such as intolerance to pitch drift and variable tempo. The proposed method is evaluated and its performance analyzed on a collection of 206 folk songs of different ensemble types: solo, two- and three-voiced, choir, instrumental, and instrumental with singing. It outperforms current commercial music segmentation methods for noninstrumental music and is on a par with the best for instrumental recordings. The method is also comparable to a more specialized method for segmentation of solo singing folk music recordings.

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“…Segments of consistent metrical structure are expected to correspond to homogeneous regions in the metergram so that segmentation may be retrieved by detection of homogeneity [10]. NMF has previously been used for homogeneity-based segmentation [11,12,13,14]. Here we present a variant of sparse-NMF which we compare to existing NMF methods as well as a popular novelty-based approach [15] to perform segmentation.…”

Section: Introductionmentioning

confidence: 99%

Tracking metrical structure changes with sparse-NMF

Quinton

O'Hanlon

Dixon

et al. 2017

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

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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. Abrupt changes of metrical structure over time are comparatively rarely addressed. In this paper, we present an unsupervised approach to detect metrical structure changes. Formulating the problem as a metrical structure based segmentation retrieval task, we present a variant of sparse NMF and compare it to existing methods. For evaluation, we introduce a new dataset of music recordings containing metric modulations with the corresponding annotations.

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Convex non-negative matrix factorization for automatic music structure identification

Cited by 23 publications

References 10 publications

Estimating the Structural Segmentation of Popular Music Pieces Under Regularity Constraints

Estimating the Structural Segmentation of Popular Music Pieces Under Regularity Constraints

Probabilistic Segmentation of Folk Music Recordings

Tracking metrical structure changes with sparse-NMF

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