4 accepted papers
We study four data augmentation (DA) techniques and two model architectures on realistic data for sound event localization and detection (SELD). First, based on ResNet-Conformer (RC), we compare the four DA approaches on the realistic DCASE 2022 SELD test set which is often not easy to handle due to…
This study focuses on the design of a loss function for a deep neural network (DNN)-based model with two branches, which is used to solve sound event localization and detection (SELD) on low-resource realistic data. To this end, we employ a secondary network for audio classification, which provides…
Single-channel speech enhancement for automatic speech recognition (ASR) has been widely studied. However, most speech enhancement methods conduct over suppression and introduce distortion, which limits performance gains or even deteriorates the back-end performance. The key to solving this problem…
In this paper, we propose a progressive learning-based adaptive noise and speech estimation (PL-ANSE) method for speech preprocessing in noisy speech recognition, leveraging upon a frame-level noise tracking capability of improved minima controlled recursive averaging (IMCRA) and an utterance-level…