Music Scene-Adaptive Harmonic Dictionary for Unsupervised Note-Event Detection

Carabias-Orti, Julio J.; Caadas-Quesada, F.J.; Ruiz-Reyes, N.

doi:10.1109/tasl.2009.2038824

Cited by 12 publications

(5 citation statements)

References 21 publications

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“…The targeted users of the primary system began with singing students and violin students who were comfortable with computers. In 2010, Carabiaset al [34] suggested an unsupervised procedure for obtaining music scene-adaptive spectral patterns for every MIDI note. In addition, the attained harmonic dictionary was deployed to note-event recognition with harmonizing pursuit.…”

Section: Othersmentioning

confidence: 99%

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A Comprehensive Review on Automatic Music Transcription: Survey of Transcription Techniques

Sagar Latake

2024

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In music, transcription is the practice of notating a piece or a sound which was previously unnotated and/or unpopular as a written music". An absolute transcription will be performed only when the timing, pitching, and instruments of all sound events are solved. In music transcription systems, a MIDI file is found to be a suitable format for melodic notations. This survey intends to make a review of 65 papers that concern music transcription using machine learning techniques. Accordingly, systematic analyses of the adopted techniques are carried out and presented briefly. The performances and related maximum achievements of each contribution are also portrayed in this survey. Moreover, the various datasets used in music transcription techniques were considered and reviewed in this work. Finally, the survey portrays the research problems and weaknesses that may be supportive for researchers to introduce the latest techniques related to music transcription.

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Section: Othersmentioning

confidence: 99%

“…From the review, it was noted that the NMF method was used in [1] [3] [10] [34] [43] [48] [55] and [62].…”

Section: Review Of Adopted Techniquesmentioning

confidence: 99%

A Comprehensive Review on Automatic Music Transcription: Survey of Transcription Techniques

Sagar Latake

2024

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“…Problems with corruption of time continuity [11] and difficulty in selecting an apt stopping condition [9] are reported when matching pursuits are employed for AMT. Indeed, it would seem that greedy pursuits may not be appropriate for AMT decompositions.…”

Section: B Backwards Eliminationmentioning

confidence: 99%

“…This datapoint dictionary is overcomplete (K > M ), and Orthogonal Matching Pursuit (OMP) [10] is used to decompose a spectrogram. Difficulty in selecting an appropriate stopping condition for OMP in this context is identified [9], while broken temporal continuity in spectrogram decompositions using greedy pursuits is reported in [11]. However, the potential advantage of stepwise pursuits in the case of multi-instrument signals is noted in [12].…”

Section: Introductionmentioning

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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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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“…Supervised [8,9] and unsupervised [10,11] learning techniques have also been investigated for this task. The matching pursuit algorithm, which approximates a solution for decomposing a signal into linear functions (atoms), is also adopted in some approaches [12,13]. Methods based on statistical inference within parametric signal models [3,14,15] have also been studied for this task.…”

Section: Introductionmentioning

confidence: 99%

Efficient methods for joint estimation of multiple fundamental frequencies in music signals

Pertusa

Iñesta

2012

EURASIP J. Adv. Signal Process.

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This study presents efficient techniques for multiple fundamental frequency estimation in music signals. The proposed methodology can infer harmonic patterns from a mixture considering interactions with other sources and evaluate them in a joint estimation scheme. For this purpose, a set of fundamental frequency candidates are first selected at each frame, and several hypothetical combinations of them are generated. Combinations are independently evaluated, and the most likely is selected taking into account the intensity and spectral smoothness of its inferred patterns. The method is extended considering adjacent frames in order to smooth the detection in time, and a pitch tracking stage is finally performed to increase the temporal coherence. The proposed algorithms were evaluated in MIREX contests yielding state of the art results with a very low computational burden.

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Music Scene-Adaptive Harmonic Dictionary for Unsupervised Note-Event Detection

Cited by 12 publications

References 21 publications

A Comprehensive Review on Automatic Music Transcription: Survey of Transcription Techniques

A Comprehensive Review on Automatic Music Transcription: Survey of Transcription Techniques

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

Efficient methods for joint estimation of multiple fundamental frequencies in music signals

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