Polyphonic Piano Music Transcription using Long Short-Term Memory

Sadekar, Aakash; Mahajan, S. P.

doi:10.1109/icccnt45670.2019.8944400

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Many studies developed hybrid methods to achieve enhanced transcription performance. Hybrid methods of Convolutional Neural Network (CNN) and RNN [6]; CNN, RNN, and LSTM [20]; CNN and Bidirectional LSTM [21]; Convolutional Recurrent Neural Network (CRNN) and Bidirectional Gate Recurrent Unit (BiGRU) have been proposed by combining various signal transformations [22], [23]. The results have proven that combining CNN in hybrid methods contributed to increasing the transcription performance.…”

Section: Related Workmentioning

confidence: 99%

IRAWNET: A Method for Transcribing Indonesian Classical Music Notes Directly from Multichannel Raw Audio

Dewi Nurdiyah,

Eko Mulyanto Yuniarno,

Yoyon Kusnendar Suprapto

et al. 2023

EMITTER Int'l J. of Engin. Technol.

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A challenging task when developing real-time Automatic Music Transcription (AMT) methods is directly leveraging inputs from multichannel raw audio without any handcrafted signal transformation and feature extraction steps. The crucial problems are that raw audio only contains an amplitude in each timestamp, and the signals of the left and right channels have different amplitude intensities and onset times. Thus, this study addressed these issues by proposing the IRawNet method with fused feature layers to merge different amplitude from multichannel raw audio. IRawNet aims to transcribe Indonesian classical music notes. It was validated with the Gamelan music dataset. The Synthetic Minority Oversampling Technique (SMOTE) overcame the class imbalance of the Gamelan music dataset. Under various experimental scenarios, the performance effects of oversampled data, hyperparameters tuning, and fused feature layers are analyzed. Furthermore, the performance of the proposed method was compared with Temporal Convolutional Network (TCN), Deep WaveNet, and the monochannel IRawNet. The results proved that proposed method almost achieves superior results in entire metric performances with 0.871 of accuracy, 0.988 of AUC, 0.927 of precision, 0.896 of recall, and 0.896 of F1 score.

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