Piano Transcription With Convolutional Sparse Lateral Inhibition

Cogliati, Andrea; Duan, Zhiyao; Wohlberg, Brendt

doi:10.1109/lsp.2017.2666183

Cited by 22 publications

(15 citation statements)

References 22 publications

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“…The parallel notes overlap in the time domain and interact in the frequency domain, which increase the complexity of the polyphonic signals. And piano which contains 88 keys or pitches is a typical polyphonic instrument, so there are also many comprehensive researches in the polyphonic piano transcription [2]. In this paper, we also focus on the polyphonic piano transcription.…”

Section: Introductionmentioning

confidence: 99%

Onset-Aware Polyphonic Piano Transcription: A CNN-Based Approach

2019

Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering

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Automatic music transcription (AMT) transforms the musical audio content into symbolic notations, including onsets, offsets and pitches. In this paper, we designed a polyphonic piano transcription system based on Convolutional Neural Network (CNN), and it improves the note-level results. Our proposed method has two advantages: Firstly, A CNN model is used to detect the onset event and align the onsets of the notes into more accurate position. Secondly, the other CNN model is used to detect the onsets of 88 notes. And we improve the model's performance by using dual-channel spectrogram as input, appropriate number of convolution layers and the weights for the positive samples in loss function. The public dataset of MAPS is adopted to train and evaluate. Finally, in the "ENSTDkCl" subset, our proposed solution achieves 85.15% on note-level F1-measure. To the best of our knowledge, the result is highest F1-measure scores in the state of art.

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Section: Introductionmentioning

confidence: 99%

Onset-Aware Polyphonic Piano Transcription: A CNN-Based Approach

2019

Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering

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“…Piano is a typical multi-pitch instrument and has a wide playing range of 88 pitches. As a challenging task in polyphonic AMT, piano transcription has been studied extensively [3].…”

Section: Introductionmentioning

confidence: 99%

Polyphonic Piano Transcription with a Note-Based Music Language Model

2018

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This paper proposes a note-based music language model (MLM) for improving note-level polyphonic piano transcription. The MLM is based on the recurrent structure, which could model the temporal correlations between notes in music sequences. To combine the outputs of the note-based MLM and acoustic model directly, an integrated architecture is adopted in this paper. We also propose an inference algorithm, in which the note-based MLM is used to predict notes at the blank onsets in the thresholding transcription results. The experimental results show that the proposed inference algorithm improves the performance of note-level transcription. We also observe that the combination of the restricted Boltzmann machine (RBM) and recurrent structure outperforms a single recurrent neural network (RNN) or long short-term memory network (LSTM) in modeling the high-dimensional note sequences. Among all the MLMs, LSTM-RBM helps the system yield the best results on all evaluation metrics regardless of the performance of acoustic models.

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“…Thirdly, generally, pitch is an independent direction by contrast with other music research directions (timbre, beat, rhythm, chord, melody) that results in pitch can be combined with other directions' methods. At present, F0 tracking can be achieved by using many methods [1] such as probabilistic latent component analysis (PLCA) [2], Nonnegative Matrix Factorization (NMF) [3], Support Vector Machines (SVM), Gaussian Mixture Model (GMM), Hidden Markov Model (HMM) [4], etc.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Based Fundamental and Harmonic Frequency Detection in Polyphonic Music Analysis

Yan

Ren

et al. 2018

Lecture Notes in Electrical Engineering

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In this paper, we present an efficient approach to detect and tracking the fundamental frequency (F0) from 'wav' audio. In general, music F0 and harmonic frequency show the multiple relations; therefore frequency domain analysis can be used to track the F0. The model includes the harmonic frequency probability analysis method and useful pre-post processing for multiple instruments. Thus, the proposed system can efficiently transcribe polyphonic music, while taking into account the probability of F0 and harmonic frequency. The experimental results demonstrate that the proposed system can successful transcribe polyphonic music, achieved the quite advanced level.

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Piano Transcription With Convolutional Sparse Lateral Inhibition

Cited by 22 publications

References 22 publications

Onset-Aware Polyphonic Piano Transcription: A CNN-Based Approach

Onset-Aware Polyphonic Piano Transcription: A CNN-Based Approach

Polyphonic Piano Transcription with a Note-Based Music Language Model

Knowledge Based Fundamental and Harmonic Frequency Detection in Polyphonic Music Analysis

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