ChordSync: Conformer-Based Alignment of Chord Annotations to Music Audio
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| Format: | Preprint |
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2024
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| _version_ | 1866913455391375360 |
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| author | Poltronieri, Andrea Presutti, Valentina Rocamora, Martín |
| author_facet | Poltronieri, Andrea Presutti, Valentina Rocamora, Martín |
| contents | In the Western music tradition, chords are the main constituent components of harmony, a fundamental dimension of music. Despite its relevance for several Music Information Retrieval (MIR) tasks, chord-annotated audio datasets are limited and need more diversity. One way to improve those resources is to leverage the large number of chord annotations available online, but this requires aligning them with music audio. However, existing audio-to-score alignment techniques, which typically rely on Dynamic Time Warping (DTW), fail to address this challenge, as they require weakly aligned data for precise synchronisation. In this paper, we introduce ChordSync, a novel conformer-based model designed to seamlessly align chord annotations with audio, eliminating the need for weak alignment. We also provide a pre-trained model and a user-friendly library, enabling users to synchronise chord annotations with audio tracks effortlessly. In this way, ChordSync creates opportunities for harnessing crowd-sourced chord data for MIR, especially in audio chord estimation, thereby facilitating the generation of novel datasets. Additionally, our system extends its utility to music education, enhancing music learning experiences by providing accurately aligned annotations, thus enabling learners to engage in synchronised musical practices. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_00674 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ChordSync: Conformer-Based Alignment of Chord Annotations to Music Audio Poltronieri, Andrea Presutti, Valentina Rocamora, Martín Sound Machine Learning Multimedia Audio and Speech Processing 68P20 I.2.6 In the Western music tradition, chords are the main constituent components of harmony, a fundamental dimension of music. Despite its relevance for several Music Information Retrieval (MIR) tasks, chord-annotated audio datasets are limited and need more diversity. One way to improve those resources is to leverage the large number of chord annotations available online, but this requires aligning them with music audio. However, existing audio-to-score alignment techniques, which typically rely on Dynamic Time Warping (DTW), fail to address this challenge, as they require weakly aligned data for precise synchronisation. In this paper, we introduce ChordSync, a novel conformer-based model designed to seamlessly align chord annotations with audio, eliminating the need for weak alignment. We also provide a pre-trained model and a user-friendly library, enabling users to synchronise chord annotations with audio tracks effortlessly. In this way, ChordSync creates opportunities for harnessing crowd-sourced chord data for MIR, especially in audio chord estimation, thereby facilitating the generation of novel datasets. Additionally, our system extends its utility to music education, enhancing music learning experiences by providing accurately aligned annotations, thus enabling learners to engage in synchronised musical practices. |
| title | ChordSync: Conformer-Based Alignment of Chord Annotations to Music Audio |
| topic | Sound Machine Learning Multimedia Audio and Speech Processing 68P20 I.2.6 |
| url | https://arxiv.org/abs/2408.00674 |