Humming-Based Music Retrieval and Generation Approaches

Query-by-humming (QbH) is a natural and intuitive approach within Music Information Retrieval (MIR), enabling users to retrieve or generate music using humming input. Unlike fingerprint- based recognition systems such as Shazam, QbH must process noisy, off-pitch, and rhythm-inconsistent queries. Over nearly three decades, research has progressed from early pitch contour and string-matching systems to modern deep learning embedding methods and generative pipelines. This survey reviews eight significant works: Ghias et al. (1995), Pham et al. (2024), Ranjan and Srivastava (2023), Tencent AI Lab (2023), Jin (2021), Amatov et al. (2023), Zhang (2024), and the Sri Lankan MIR review (2021). We analyze their methodologies, datasets, performance, contributions, and limitations. The review concludes that deep embedding-based retrieval provides the best accuracy, but hybrid systems combining noise-robust preprocessing, dataset scaling, and generative mashups offer the most promise for future applications such as Hummify.