Automatic Music Transcription using Convolutional Neural Networks and Constant-Q transform
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908353006927872 |
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| author | Telila, Yohannis Cucinotta, Tommaso Bacciu, Davide |
| author_facet | Telila, Yohannis Cucinotta, Tommaso Bacciu, Davide |
| contents | Automatic music transcription (AMT) is the problem of analyzing an audio recording of a musical piece and detecting notes that are being played. AMT is a challenging problem, particularly when it comes to polyphonic music. The goal of AMT is to produce a score representation of a music piece, by analyzing a sound signal containing multiple notes played simultaneously. In this work, we design a processing pipeline that can transform classical piano audio files in .wav format into a music score representation. The features from the audio signals are extracted using the constant-Q transform, and the resulting coefficients are used as an input to the convolutional neural network (CNN) model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04451 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automatic Music Transcription using Convolutional Neural Networks and Constant-Q transform Telila, Yohannis Cucinotta, Tommaso Bacciu, Davide Sound Artificial Intelligence Machine Learning Multimedia Audio and Speech Processing Automatic music transcription (AMT) is the problem of analyzing an audio recording of a musical piece and detecting notes that are being played. AMT is a challenging problem, particularly when it comes to polyphonic music. The goal of AMT is to produce a score representation of a music piece, by analyzing a sound signal containing multiple notes played simultaneously. In this work, we design a processing pipeline that can transform classical piano audio files in .wav format into a music score representation. The features from the audio signals are extracted using the constant-Q transform, and the resulting coefficients are used as an input to the convolutional neural network (CNN) model. |
| title | Automatic Music Transcription using Convolutional Neural Networks and Constant-Q transform |
| topic | Sound Artificial Intelligence Machine Learning Multimedia Audio and Speech Processing |
| url | https://arxiv.org/abs/2505.04451 |