Detecting Musical Deepfakes
This addresses the challenge of musical deepfakes for musicians and the music industry, though it appears incremental as it applies existing methods to a new domain.
This study tackled the problem of detecting AI-generated songs using the FakeMusicCaps dataset, achieving classification of audio as deepfake or human with results presented under adversarial conditions involving tempo stretching and pitch shifting.
The proliferation of Text-to-Music (TTM) platforms has democratized music creation, enabling users to effortlessly generate high-quality compositions. However, this innovation also presents new challenges to musicians and the broader music industry. This study investigates the detection of AI-generated songs using the FakeMusicCaps dataset by classifying audio as either deepfake or human. To simulate real-world adversarial conditions, tempo stretching and pitch shifting were applied to the dataset. Mel spectrograms were generated from the modified audio, then used to train and evaluate a convolutional neural network. In addition to presenting technical results, this work explores the ethical and societal implications of TTM platforms, arguing that carefully designed detection systems are essential to both protecting artists and unlocking the positive potential of generative AI in music.