A post-processing of onset detection based on verification with neural network

Lin, Mingtai; Yin, Feng

doi:10.1007/978-981-15-2756-2_6

Cited by 1 publication

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“…Data-driven methods build statistical models for note onsets by employing machine learning methods on a large set of training data. For instance, learning a probabilistic (hidden Markov) model exploiting the rhythmic regularity in music [13], training a neural network (recurrent [14], convolutional [15][16][17][18]), or learning dictionaries describing onset/non-onset patterns [7]. These machine learning methods can either solve a classification problem, differentiating onsets from non-onsets, or a regression problem of which the output is then used to estimate a suitable ODF.…”

Section: Introductionmentioning

confidence: 99%

Musical note onset detection based on a spectral sparsity measure

Mounir

Karsmakers

Waterschoot

2021

J AUDIO SPEECH MUSIC PROC.

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If music is the language of the universe, musical note onsets may be the syllables for this language. Not only do note onsets define the temporal pattern of a musical piece, but their time-frequency characteristics also contain rich information about the identity of the musical instrument producing the notes. Note onset detection (NOD) is the basic component for many music information retrieval tasks and has attracted significant interest in audio signal processing research. In this paper, we propose an NOD method based on a novel feature coined as Normalized Identification of Note Onset based on Spectral Sparsity (NINOS2). The NINOS2 feature can be thought of as a spectral sparsity measure, aiming to exploit the difference in spectral sparsity between the different parts of a musical note. This spectral structure is revealed when focusing on low-magnitude spectral components that are traditionally filtered out when computing note onset features. We present an extensive set of NOD simulation results covering a wide range of instruments, playing styles, and mixing options. The proposed algorithm consistently outperforms the baseline Logarithmic Spectral Flux (LSF) feature for the most difficult group of instruments which are the sustained-strings instruments. It also shows better performance for challenging scenarios including polyphonic music and vibrato performances.

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