Piano Transcription With Transformers

We study the performance of two methods for sequence-to-sequence piano transcription using Transformers. One method focuses on the analysis of a single piano note, while the other is based on a sequence of notes. In both cases, the results indicate good generalization.

This approach combines a music language model with an advanced neural network. This method can learn how to perform different transcription tasks by learning to recognize the musical elements. The output vocabulary of the system can be changed without changing the system’s architecture. It can also be trained to transcribe multiple instruments, greatly extending its application.

Score-informed piano transcription is a process that can be used for generating piano transcriptions from recordings. Using a dataset such as MAESTRO, it is possible to train a machine learning model on piano transcription data. The dataset is obtained from a piano known as the Yamaha Disklavier, which allows recording of audio and MIDI simultaneously. This dataset also enables the training of deep learning methods.

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The resulting music snippets are then used to create a MIDI file of the piece. This file contains information such as note pitch, time value, and polyphony. By combining this information with the information from the piano score, a MIDI file can be created that enables piano transcription.

Sequence-To-Sequence Piano Transcription With Transformers

Currently, automatic music transcription systems convert audio recordings into music notation. However, the accuracy of the results is still far from human transcription. Although certain applications incorporate score information into the resulting music, the results are still significantly below the level achieved by human transcription. A score-informed approach can eliminate mistakes made during practice and provide a more accurate assessment of a student’s performance.

Informed piano transcription can help eliminate basic mistakes made during practice sessions and allow a pianist to focus on more advanced concepts. However, it has received only limited attention in the past. AMT can be used in a variety of applications, including music teaching, musicology, and architectural studies. The MIReS project has received funding from the European Commission and the Westfield Trust.

Using this method, MIDI transcribed recordings can be converted to audio. The resulting music can be used to recreate a performance with the help of MIDI. However, when pianists play with varied styles, their playing styles vary. The Vienna 4×22 Piano Corpus contains 88 MIDI and audio files. This corpus was developed by 22 advanced student and professional pianists.

The MUSHRA test also provides a comparison between various methods. It involves a reference audio file and four candidate audio files. The candidates were then rated by subjects. One was rated higher than the others. It is also important to consider the level of the performers. For example, students from the highest level of music education rated 25 s and 50 s excerpts significantly higher than those from high school.