H
Haomin Zhang
Researcher at University of Science and Technology of China
Publications - 6
Citations - 510
Haomin Zhang is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Spectrogram & Noise. The author has an hindex of 4, co-authored 5 publications receiving 421 citations.
Papers
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Journal ArticleDOI
Robust sound event classification using deep neural networks
TL;DR: A sound event classification framework is outlined that compares auditory image front end features with spectrogram image-based frontEnd features, using support vector machine and deep neural network classifiers, and is shown to compare very well with current state-of-the-art classification techniques.
Proceedings ArticleDOI
Robust sound event recognition using convolutional neural networks
TL;DR: This work proposes novel features derived from spectrogram energy triggering, allied with the powerful classification capabilities of a convolutional neural network (CNN), which demonstrates excellent performance under noise-corrupted conditions when compared against state-of-the-art approaches on standard evaluation tasks.
Journal ArticleDOI
Continuous robust sound event classification using time-frequency features and deep learning
TL;DR: This paper proposes and evaluates a novel Bayesian-inspired front end for the segmentation and detection of continuous sound recordings prior to classification, and benchmarks several high performing isolated sound classifiers to operate with continuous sound data by incorporating an energy-based event detection front end.
Journal ArticleDOI
A new variance-based approach for discriminative feature extraction in machine hearing classification using spectrogram features
TL;DR: A novel data-driven feature extraction method that uses variance-based criteria to define spectral pooling of features from spectrograms is explored, and is shown to achieve very good performance for robust classification.
Proceedings ArticleDOI
Robust Sound Event Detection in Continuous Audio Environments.
TL;DR: This paper proposes and evaluates the use of spectrogram image features employing an energy detector to segment sound events, before developing a novel segmentation method making use of a Bayesian inference criteria.