Search Results for author: Chieh-Chi Kao

Found 9 papers, 0 papers with code

Federated Self-Supervised Learning for Acoustic Event Classification

no code implementations22 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.

Classification Continual Learning +2

On Front-end Gain Invariant Modeling for Wake Word Spotting

no code implementations13 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.

Compression of Acoustic Event Detection Models With Quantized Distillation

no code implementations1 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.

Event Detection Knowledge Distillation +1

Compression of Acoustic Event Detection Models with Low-rank Matrix Factorization and Quantization Training

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.

Event Detection Quantization

Patch-Based Image Hallucination for Super Resolution with Detail Reconstruction from Similar Sample Images

no code implementations3 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.

Super-Resolution

Localization-Aware Active Learning for Object Detection

no code implementations16 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.

Active Learning Classification +5

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