7 citations · 24 across the 16 of their papers we have counts for
16 papers
Weight-sharing Supernet for Searching Specialized Acoustic Event Classification Networks Across Device Constraints
Guan-Ting Lin, Qingming Tang, Chieh-Chi Kao +2
Acoustic Event Classification (AEC) has been widely used in devices such as smart speakers and mobile phones for home safety or accessibility support. As AEC models run on more and…
Impact of Acoustic Event Tagging on Scene Classification in a Multi-Task Learning Framework
Rahil Parikh, Harshavardhan Sundar, Ming Sun +2
Acoustic events are sounds with well-defined spectro-temporal characteristics which can be associated with the physical objects generating them. Acoustic scenes are collections of…
Federated Self-Supervised Learning for Acoustic Event Classification
Meng Feng, Chieh-Chi Kao, Qingming Tang +4
Standard acoustic event classification (AEC) solutions require large-scale collection of data from client devices for model optimization. Federated learning (FL) is a compelling fr…
Towards High-fidelity Singing Voice Conversion with Acoustic Reference and Contrastive Predictive Coding
Chao Wang, Zhonghao Li, Benlai Tang +4
Recently, phonetic posteriorgrams (PPGs) based methods have been quite popular in non-parallel singing voice conversion systems. However, due to the lack of acoustic information in…
Multi-Task Self-Supervised Pre-Training for Music Classification
Ho-Hsiang Wu, Chieh-Chi Kao, Qingming Tang +4
Deep learning is very data hungry, and supervised learning especially requires massive labeled data to work well. Machine listening research often suffers from limited labeled data…
Intra-Utterance Similarity Preserving Knowledge Distillation for Audio Tagging
Chun-Chieh Chang, Chieh-Chi Kao, Ming Sun +1
Knowledge Distillation (KD) is a popular area of research for reducing the size of large models while still maintaining good performance. The outputs of larger teacher models are u…