Non-local musical statistics as guides for audio-to-score piano transcription

Shibata, Kentaro; Nakamura, Eijiro; Yoshii, Kazuyoshi

doi:10.1016/j.ins.2021.03.014

Cited by 16 publications

(4 citation statements)

References 25 publications

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“…the automata constructions are performed on-the-fly during best-search for efficiency reasons. One advantage of this swVPA approach is the global view provided by the stack during transcription, as opposed to other HMM-based approaches [22].…”

Section: Discussionmentioning

confidence: 99%

Symbolic Weighted Language Models, Quantitative Parsing and Automated Music Transcription

Jacquemard

Nava

2022

Implementation and Application of Automata

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We study several classes of symbolic weighted formalisms: automata (swA), transducers (swT) and visibly pushdown extensions (swVPA, swVPT). They combine the respective extensions of their symbolic and weighted counterparts, allowing a quantitative evaluation of words over a large or infinite input alphabet. We present properties of closure by composition, the computation of transducer-defined distances between nested words and languages, as well as a PTIME 1-best search algorithm for swVPA. These results are applied to solve in PTIME a variant of parsing over infinite alphabets. We illustrate this approach with a motivating use case in automated music transcription.

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Section: Discussionmentioning

confidence: 99%

Symbolic Weighted Language Models, Quantitative Parsing and Automated Music Transcription

Jacquemard

Nava

2022

Implementation and Application of Automata

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“…We note that in related studies on rhythm transcription, researchers also apply HMM-based methods to take an input signal and estimate the metrical positions of all musical notes. In particular, similar first-order Markov assumptions and tempo transition probability distributions are adopted [38], [39], [40]. Despite that local tempo and local tempo changes are parameterized in these models, parameters are determined globally (e.g., based on a dataset) without the knowledge of "local periodicity" (which is explicitly extracted based on small local windows) proposed in this work.…”

Section: B Ppts With Global Assumptions For Tempomentioning

confidence: 99%

Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

Chiu

Müller

Davies

et al. 2023

IEEE/ACM Trans. Audio Speech Lang. Process.

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To model the periodicity of beats, state-of-the-art beat tracking systems use "post-processing trackers" (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo. For expressive classical music, however, these assumptions can be too rigid. With two large datasets of Western classical piano music, namely the Aligned Scores and Performances (ASAP) dataset and a dataset of Chopin's Mazurkas (Maz-5), we report on experiments showing the failure of existing PPTs to cope with local tempo changes, thus calling for new methods. In this paper, we propose a new local periodicity-based PPT, called predominant local pulse-based dynamic programming (PLPDP) tracking, that allows for more flexible tempo transitions. Specifically, the new PPT incorporates a method called "predominant local pulses" (PLP) in combination with a dynamic programming (DP) component to jointly consider the locally detected periodicity and beat activation strength at each time instant. Accordingly, PLPDP accounts for the local periodicity, rather than relying on a global tempo assumption. Compared to existing PPTs, PLPDP particularly enhances the recall values at the cost of a lower precision, resulting in an overall improvement of F1-score for beat tracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838).

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“…Owing to the recent development of automatic music transcription [23][24][25], accurate score information can be extracted from polyphonic music signals. Since the proposed method uses the interpretable synthesis parameters, we can utilize the available score information for the initialization of fr,t and lr,t.…”

Section: Initialization Of F0 and Loudness Using Score Informationmentioning

confidence: 99%

Differentiable Digital Signal Processing Mixture Model for Synthesis Parameter Extraction from Mixture of Harmonic Sounds

Kawamura¹,

Nakamura²,

Kitamura³

et al. 2022

Preprint

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A differentiable digital signal processing (DDSP) autoencoder is a musical sound synthesizer that combines a deep neural network (DNN) and spectral modeling synthesis. It allows us to flexibly edit sounds by changing the fundamental frequency, timbre feature, and loudness (synthesis parameters) extracted from an input sound. However, it is designed for a monophonic harmonic sound and cannot handle mixtures of harmonic sounds. In this paper, we propose a model (DDSP mixture model) that represents a mixture as the sum of the outputs of multiple pretrained DDSP autoencoders. By fitting the output of the proposed model to the observed mixture, we can directly estimate the synthesis parameters of each source. Through synthesis parameter extraction experiments, we show that the proposed method has high and stable performance compared with a straightforward method that applies the DDSP autoencoder to the signals separated by an audio source separation method.

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Non-local musical statistics as guides for audio-to-score piano transcription

Cited by 16 publications

References 25 publications

Symbolic Weighted Language Models, Quantitative Parsing and Automated Music Transcription

Symbolic Weighted Language Models, Quantitative Parsing and Automated Music Transcription

Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

Differentiable Digital Signal Processing Mixture Model for Synthesis Parameter Extraction from Mixture of Harmonic Sounds

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