abstract
This paper explores the application of artificial intelligence (AI) and machine learning (ML) in the music industry, focusing on analytical and generative tools. The study is divided into two main sections: Music Recommendation Systems (MRS) and Music Generation Systems (MGS). In the first section, the paper examines the role of AI and ML in enhancing MRS, which have revolutionized music consumption by providing personalized recommendations. Techniques such as Collaborative Filtering (CF), Content-based Filtering (CBF), and advanced deep learning models are discussed. The paper also highlights the BaRT algorithm used by Spotify as a case study, demonstrating the practical implementation of these technologies. The second section delves into generative AI for music creation. It explores computational creativity, emphasizing the distinction between music composition and synthesis. Various methods, including Markov Chains, Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks, are analyzed for their effectiveness in generating music. The Google Magenta project is presented as a case study, showcasing advanced tools like Performance RNN and NSynth for creating expressive and innovative musical pieces.
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