Search Results for author: Jibin Wu

Found 25 papers, 8 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 +1

Typing to Listen at the Cocktail Party: Text-Guided Target Speaker Extraction

1 code implementation11 Oct 2023 Xiang Hao, Jibin Wu, Jianwei Yu, Chenglin Xu, Kay Chen Tan

However, the effectiveness of these models is hindered in real-world scenarios due to the unreliable or even absence of pre-registered cues.

Language Modelling Large Language Model +1

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 Automatic Speech Recognition (ASR) +1

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

Training Spiking Neural Networks with Local Tandem Learning

1 code implementation10 Oct 2022 Qu Yang, Jibin Wu, Malu Zhang, Yansong Chua, Xinchao Wang, Haizhou Li

The LTL rule follows the teacher-student learning approach by mimicking the intermediate feature representations of a pre-trained ANN.

A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks

1 code implementation26 May 2023 Xinyi Chen, Qu Yang, Jibin Wu, Haizhou Li, Kay Chen Tan

As an initial exploration in this direction, we propose a hybrid neural coding and learning framework, which encompasses a neural coding zoo with diverse neural coding schemes discovered in neuroscience.

Image Classification

TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling

1 code implementation25 Aug 2023 Shimin Zhang, Qu Yang, Chenxiang Ma, Jibin Wu, Haizhou Li, Kay Chen Tan

The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays.

Scaling Supervised Local Learning with Augmented Auxiliary Networks

1 code implementation27 Feb 2024 Chenxiang Ma, Jibin Wu, Chenyang Si, Kay Chen Tan

AugLocal constructs each hidden layer's auxiliary network by uniformly selecting a small subset of layers from its subsequent network layers to enhance their synergy.

Image Classification

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.

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.

Benchmarking speech-recognition +1

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

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

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.

Computational Efficiency Image Reconstruction +2

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.

Long Short-term Memory with Two-Compartment Spiking Neuron

no code implementations14 Jul 2023 Shimin Zhang, Qu Yang, Chenxiang Ma, Jibin Wu, Haizhou Li, Kay Chen Tan

The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays.

Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding

no code implementations29 Aug 2023 Xinyi Chen, Jibin Wu, Huajin Tang, Qinyuan Ren, Kay Chen Tan

The human brain exhibits remarkable abilities in integrating temporally distant sensory inputs for decision-making.

Decision Making

Spiking-LEAF: A Learnable Auditory front-end for Spiking Neural Networks

no code implementations18 Sep 2023 Zeyang Song, Jibin Wu, Malu Zhang, Mike Zheng Shou, Haizhou Li

Brain-inspired spiking neural networks (SNNs) have demonstrated great potential for temporal signal processing.

Keyword Spotting Speaker Identification

Delayed Memory Unit: Modelling Temporal Dependency Through Delay Gate

no code implementations23 Oct 2023 Pengfei Sun, Jibin Wu, Malu Zhang, Paul Devos, Dick Botteldooren

Recurrent Neural Networks (RNNs) are renowned for their adeptness in modeling temporal dependencies, a trait that has driven their widespread adoption for sequential data processing.

Gesture Recognition Sequential Image Classification +2

LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding

no code implementations23 Oct 2023 Qu Yang, Malu Zhang, Jibin Wu, Kay Chen Tan, Haizhou Li

With TTFS coding, we can achieve up to orders of magnitude saving in computation over ANN and other rate-based SNNs.

Edge-computing Image Classification +2

Deep Convolutional Spiking Neural Networks for Keyword Spotting

no code implementations Interspeech 2020 Emre Yilmaz, Özgür Bora Gevrek, Jibin Wu, Yuxiang Chen, Xuanbo Meng, Haizhou Li

To explore the effectiveness and computational complexity of SNN on KWS and wakeword detection, we compare the performance and computational costs of spiking fully-connected and convolutional neural networks with ANN counterparts under clean and noisy testing conditions.

Keyword Spotting

Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation

no code implementations22 Nov 2023 Xingyu Wu, Yan Zhong, Jibin Wu, Bingbing Jiang, Kay Chen Tan

Following the extraction of embedding vectors for both algorithms and problems, the most suitable algorithm is determined through calculations of matching degrees.

AutoML Language Modelling +1

Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap

no code implementations18 Jan 2024 Xingyu Wu, Sheng-hao Wu, Jibin Wu, Liang Feng, Kay Chen Tan

Large Language Models (LLMs) have not only revolutionized natural language processing but also extended their prowess to various domains, marking a significant stride towards artificial general intelligence.

Code Generation Evolutionary Algorithms +4

Efficient Online Learning for Networks of Two-Compartment Spiking Neurons

no code implementations25 Feb 2024 Yujia Yin, Xinyi Chen, Chenxiang Ma, Jibin Wu, Kay Chen Tan

The brain-inspired Spiking Neural Networks (SNNs) have garnered considerable research interest due to their superior performance and energy efficiency in processing temporal signals.

Event-Driven Learning for Spiking Neural Networks

no code implementations1 Mar 2024 Wenjie Wei, Malu Zhang, Jilin Zhang, Ammar Belatreche, Jibin Wu, Zijing Xu, Xuerui Qiu, Hong Chen, Yang Yang, Haizhou Li

Specifically, we introduce two novel event-driven learning methods: the spike-timing-dependent event-driven (STD-ED) and membrane-potential-dependent event-driven (MPD-ED) algorithms.

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