25 citations · 40 across the 24 of their papers we have counts for
5 papers · 1 filter
Single-channel speech enhancement using learnable loss mixup
Oscar Chang, Dung N. Tran, Kazuhito Koishida
Generalization remains a major problem in supervised learning of single-channel speech enhancement. In this work, we propose learnable loss mixup (LLM), a simple and effortless tra…
Automatic Disfluency Detection from Untranscribed Speech
Amrit Romana, Kazuhito Koishida, Emily Mower Provost
Speech disfluencies, such as filled pauses or repetitions, are disruptions in the typical flow of speech. Stuttering is a speech disorder characterized by a high rate of disfluenci…
A Training Framework for Stereo-Aware Speech Enhancement using Deep Neural Networks
Bahareh Tolooshams, Kazuhito Koishida
Deep learning-based speech enhancement has shown unprecedented performance in recent years. The most popular mono speech enhancement frameworks are end-to-end networks mapping the…
Augmented Contrastive Self-Supervised Learning for Audio Invariant Representations
Melikasadat Emami, Dung Tran, Kazuhito Koishida
Improving generalization is a major challenge in audio classification due to labeled data scarcity. Self-supervised learning (SSL) methods tackle this by leveraging unlabeled data…
Sound Event Detection in Multichannel Audio using Convolutional Time-Frequency-Channel Squeeze and Excitation
Wei Xia, Kazuhito Koishida
In this study, we introduce a convolutional time-frequency-channel "Squeeze and Excitation" (tfc-SE) module to explicitly model inter-dependencies between the time-frequency domain…