Spectrogram-Based Detection of Auto-Tuned Vocals in Music Recordings
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909132605358080 |
|---|---|
| author | Gohari, Mahyar Bestagini, Paolo Benini, Sergio Adami, Nicola |
| author_facet | Gohari, Mahyar Bestagini, Paolo Benini, Sergio Adami, Nicola |
| contents | In the domain of music production and audio processing, the implementation of automatic pitch correction of the singing voice, also known as Auto-Tune, has significantly transformed the landscape of vocal performance. While auto-tuning technology has offered musicians the ability to tune their vocal pitches and achieve a desired level of precision, its use has also sparked debates regarding its impact on authenticity and artistic integrity. As a result, detecting and analyzing Auto-Tuned vocals in music recordings has become essential for music scholars, producers, and listeners. However, to the best of our knowledge, no prior effort has been made in this direction. This study introduces a data-driven approach leveraging triplet networks for the detection of Auto-Tuned songs, backed by the creation of a dataset composed of original and Auto-Tuned audio clips. The experimental results demonstrate the superiority of the proposed method in both accuracy and robustness compared to Rawnet2, an end-to-end model proposed for anti-spoofing and widely used for other audio forensic tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_05380 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spectrogram-Based Detection of Auto-Tuned Vocals in Music Recordings Gohari, Mahyar Bestagini, Paolo Benini, Sergio Adami, Nicola Sound Computer Vision and Pattern Recognition Audio and Speech Processing In the domain of music production and audio processing, the implementation of automatic pitch correction of the singing voice, also known as Auto-Tune, has significantly transformed the landscape of vocal performance. While auto-tuning technology has offered musicians the ability to tune their vocal pitches and achieve a desired level of precision, its use has also sparked debates regarding its impact on authenticity and artistic integrity. As a result, detecting and analyzing Auto-Tuned vocals in music recordings has become essential for music scholars, producers, and listeners. However, to the best of our knowledge, no prior effort has been made in this direction. This study introduces a data-driven approach leveraging triplet networks for the detection of Auto-Tuned songs, backed by the creation of a dataset composed of original and Auto-Tuned audio clips. The experimental results demonstrate the superiority of the proposed method in both accuracy and robustness compared to Rawnet2, an end-to-end model proposed for anti-spoofing and widely used for other audio forensic tasks. |
| title | Spectrogram-Based Detection of Auto-Tuned Vocals in Music Recordings |
| topic | Sound Computer Vision and Pattern Recognition Audio and Speech Processing |
| url | https://arxiv.org/abs/2403.05380 |