Search Results for author: Jibin Wu

Found 10 papers, 3 papers with code

Target Speaker Verification with Selective Auditory Attention for Single and Multi-talker Speech

1 code implementation30 Mar 2021 Chenglin Xu, Wei Rao, Jibin Wu, Haizhou Li

Inspired by the study on target speaker extraction, e. g., SpEx, we propose a unified speaker verification framework for both single- and multi-talker speech, that is able to pay selective auditory attention to the target speaker.

Multi-Task Learning Speaker Verification

Multi-Tones' Phase Coding (MTPC) of Interaural Time Difference by Spiking Neural Network

no code implementations7 Jul 2020 Zihan Pan, Malu Zhang, Jibin Wu, Haizhou Li

Inspired by the mammal's auditory localization pathway, in this paper we propose a pure spiking neural network (SNN) based computational model for precise sound localization in the noisy real-world environment, and implement this algorithm in a real-time robotic system with a microphone array.

Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks

no code implementations2 Jul 2020 Jibin Wu, Cheng-Lin Xu, Daquan Zhou, Haizhou Li, Kay Chen Tan

In this paper, we propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition, which is referred to as progressive tandem learning of deep SNNs.

Image Reconstruction Object Recognition +1

Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks

no code implementations26 Mar 2020 Malu Zhang, Jiadong Wang, Burin Amornpaisannon, Zhixuan Zhang, VPK Miriyala, Ammar Belatreche, Hong Qu, Jibin Wu, Yansong Chua, Trevor E. Carlson, Haizhou Li

In STDBP algorithm, the timing of individual spikes is used to convey information (temporal coding), and learning (back-propagation) is performed based on spike timing in an event-driven manner.

Decision Making

Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition

1 code implementation19 Nov 2019 Jibin Wu, Emre Yilmaz, Malu Zhang, Haizhou Li, Kay Chen Tan

The brain-inspired spiking neural networks (SNN) closely mimic the biological neural networks and can operate on low-power neuromorphic hardware with spike-based computation.

Automatic Speech Recognition speech-recognition

Neural Population Coding for Effective Temporal Classification

no code implementations12 Sep 2019 Zihan Pan, Jibin Wu, Yansong Chua, Malu Zhang, Haizhou Li

We show that, with population neural codings, the encoded patterns are linearly separable using the Support Vector Machine (SVM).

Classification General Classification

An efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks

no code implementations3 Sep 2019 Zihan Pan, Yansong Chua, Jibin Wu, Malu Zhang, Haizhou Li, Eliathamby Ambikairajah

The neural encoding scheme, that we call Biologically plausible Auditory Encoding (BAE), emulates the functions of the perceptual components of the human auditory system, that include the cochlear filter bank, the inner hair cells, auditory masking effects from psychoacoustic models, and the spike neural encoding by the auditory nerve.

speech-recognition Speech Recognition

A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks

1 code implementation2 Jul 2019 Jibin Wu, Yansong Chua, Malu Zhang, Guoqi Li, Haizhou Li, Kay Chen Tan

Spiking neural networks (SNNs) represent the most prominent biologically inspired computing model for neuromorphic computing (NC) architectures.

Event-based vision

Deep Spiking Neural Network with Spike Count based Learning Rule

no code implementations15 Feb 2019 Jibin Wu, Yansong Chua, Malu Zhang, Qu Yang, Guoqi Li, Haizhou Li

Deep spiking neural networks (SNNs) support asynchronous event-driven computation, massive parallelism and demonstrate great potential to improve the energy efficiency of its synchronous analog counterpart.

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