Paper ID: 2410.05455

Dynamic HumTrans: Humming Transcription Using CNNs and Dynamic Programming

Shubham Gupta, Isaac Neri Gomez-Sarmiento, Faez Amjed Mezdari, Mirco Ravanelli, Cem Subakan

We propose a novel approach for humming transcription that combines a CNN-based architecture with a dynamic programming-based post-processing algorithm, utilizing the recently introduced HumTrans dataset. We identify and address inherent problems with the offset and onset ground truth provided by the dataset, offering heuristics to improve these annotations, resulting in a dataset with precise annotations that will aid future research. Additionally, we compare the transcription accuracy of our method against several others, demonstrating state-of-the-art (SOTA) results. All our code and corrected dataset is available at this https URL

Submitted: Oct 7, 2024