Detecting Musical Deepfakes

Ab s tract -- The proliferation of Text - to - Music (TTM) platforms has democratized music creation, letting users effortlessly generat e high - quality compositions . However, this innovation has also introduced challenges to musicians and the music in dustry . T his research focuses on utilizing the FakeMusicCaps dataset to address the challenge of detecting AI - generated songs by classifying the audio as deepfake or human. To simulate a real - world adversarial entity tempo stretching and pitch shifting modifications were applied to the dataset . Mel Spectrograms were generated from the resulting datasets, w hich were then used to train and test a convolutional neural network. This paper also explores the ethical and societal implications of TTM platforms, suggesting that detection systems developed and employed with care are a necessary tool to safeguard musicians and foster the positive potential of TTM plat forms and gen erative AI in music . Rapid a dvances in g e nerative AI have caused the creat ive landscape to be u pended, enabling almost anyone to easily create music that can be hard to distinguish from human - ma de compositions . AI - generated music is part of a wider classification of AI - generated media and art that falls unde r the category of " deepfake " .