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To manage your alert preferences, click on the button below. Manage my AlertsAutomatic Music Transcription (AMT) is an important task in Music Information Retrieval (MIR). Many researchers have focused on the structure of Convolutional Neural Network (CNN) for transcription. In this paper, we construct a CNN-based (EB) piano music transcription model using the energy-balanced constant Q transform spectrogram, which is called EB-enhanced CNN model. Unlike standard CNN-based methods, the proposed model makes the energy of the input features more balanced, so that many previously missed pitches due to weak energy can be successfully detected. Training and evaluation are performed on the MAPS dataset, a public dataset for piano transcription. As a result, our technique achieves a 3.53% f1 score improvement compared with the state-of-the-art method on the MAPS ENSTDkCl subset.
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HPCCT & BDAI '20: Proceedings of the 2020 4th High Performance Computing and Cluster Technologies Conference & 2020 3rd International Conference on Big Data and Artificial Intelligence
Recently, automatic music transcription aiming to convert acoustic music signals into symbolic notations attracts increasing attention. In order to deal with the challenges of automatic music transcription based on acoustic information, traditional .
One important problem in Musical Information Retrieval is Automatic Music Transcription, which is an automated conversion process from played music to a symbolic notation such as sheet music. Since the accuracy of previous audio-based transcription .
ACSAT '12: Proceedings of the 2012 International Conference on Advanced Computer Science Applications and Technologies
This paper presents a system that helps in automatically generating guitar tablatures and musical scores based on musical audio data. Information gathered from the audio consists of pitch, onsets and durations, chords, and beat and tempo. Major issues .