no code implementations • 16 Jan 2018 • Chieh-Chi Kao, Teng-Yok Lee, Pradeep Sen, Ming-Yu Liu
Active learning - a class of algorithms that iteratively searches for the most informative samples to include in a training dataset - has been shown to be effective at annotating data for image classification.
no code implementations • 3 Jun 2018 • Chieh-Chi Kao, Yu-Xiang Wang, Jonathan Waltman, Pradeep Sen
Image hallucination and super-resolution have been studied for decades, and many approaches have been proposed to upsample low-resolution images using information from the images themselves, multiple example images, or large image databases.
no code implementations • 29 Apr 2019 • Bowen Shi, Ming Sun, Chieh-Chi Kao, Viktor Rozgic, Spyros Matsoukas, Chao Wang
This paper presents our work of training acoustic event detection (AED) models using unlabeled dataset.
no code implementations • NIPS Workshop CDNNRIA 2018 • Bowen Shi, Ming Sun, Chieh-Chi Kao, Viktor Rozgic, Spyros Matsoukas, Chao Wang
In this paper, we present a compression approach based on the combination of low-rank matrix factorization and quantization training, to reduce complexity for neural network based acoustic event detection (AED) models.
no code implementations • 1 Jul 2019 • Bowen Shi, Ming Sun, Chieh-Chi Kao, Viktor Rozgic, Spyros Matsoukas, Chao Wang
Acoustic Event Detection (AED), aiming at detecting categories of events based on audio signals, has found application in many intelligent systems.
no code implementations • 21 Feb 2020 • Bowen Shi, Ming Sun, Krishna C. Puvvada, Chieh-Chi Kao, Spyros Matsoukas, Chao Wang
We study few-shot acoustic event detection (AED) in this paper.
no code implementations • 13 Oct 2020 • Yixin Gao, Noah D. Stein, Chieh-Chi Kao, Yunliang Cai, Ming Sun, Tao Zhang, Shiv Vitaladevuni
Since the WW model is trained with the AFE-processed audio data, its performance is sensitive to AFE variations, such as gain changes.
no code implementations • 5 Feb 2021 • Ho-Hsiang Wu, Chieh-Chi Kao, Qingming Tang, Ming Sun, Brian McFee, Juan Pablo Bello, Chao Wang
Deep learning is very data hungry, and supervised learning especially requires massive labeled data to work well.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +7
no code implementations • 22 Mar 2022 • Meng Feng, Chieh-Chi Kao, Qingming Tang, Ming Sun, Viktor Rozgic, Spyros Matsoukas, Chao Wang
Standard acoustic event classification (AEC) solutions require large-scale collection of data from client devices for model optimization.