Category-Adapted Sound Event Enhancement with Weakly Labeled Data

Abstract

Previous audio enhancement training usually requires clean signals with additive noises; hence commonly focuses on speech enhancement, where clean speech is easy to access. This paper goes beyond a broader sound event enhancement by using a weakly supervised approach via sound event detection (SED) to approximate the location and presence of a specific sound event. We propose a category-adapted system to enable enhancement on any selected sound category, where we first familiarize the model to all common sound classes and followed by a category-specific fine-tune procedure to enhance the targeted sound class. Evaluation is conducted on ten common sound classes, with a comparison to traditional and weakly supervised enhancement methods. Results indicate an average 2.86 dB SDR increase, with more significant improvement on speech (9.15 dB), music (5.01 dB), and typewriter (3.68 dB) under SNR of 0 dB. All enhancement metrics outperform previous weakly supervised methods and achieve comparable results to the state-of-the-art method that requires clean signals.