Spectrogram-Based Detection of Auto-Tuned Vocals in Music Recordings
Mahyar Gohari Paolo Bestagini Sergio Benini Nicola Adami
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.