Dynamic HumTrans: Humming Transcription Using CNNs and Dynamic Programming
Machine Learning
2024-10-10 v1 Artificial Intelligence
Sound
Audio and Speech Processing
Abstract
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 https://github.com/shubham-gupta-30/humming_transcription
Cite
@article{arxiv.2410.05455,
title = {Dynamic HumTrans: Humming Transcription Using CNNs and Dynamic Programming},
author = {Shubham Gupta and Isaac Neri Gomez-Sarmiento and Faez Amjed Mezdari and Mirco Ravanelli and Cem Subakan},
journal= {arXiv preprint arXiv:2410.05455},
year = {2024}
}