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

• URL: http://arxiv.org/abs/2007.03274v1
• Date: Tue, 7 Jul 2020 08:22:56 GMT
• Title: Multi-Tones' Phase Coding (MTPC) of Interaural Time Difference by Spiking Neural Network
• Authors: Zihan Pan, Malu Zhang, Jibin Wu, Haizhou Li
• Abstract summary: We propose a pure spiking neural network (SNN) based computational model for precise sound localization in the noisy real-world environment. We implement this algorithm in a real-time robotic system with a microphone array. The experiment results show a mean error azimuth of 13 degrees, which surpasses the accuracy of the other biologically plausible neuromorphic approach for sound source localization.
• Score: 68.43026108936029
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. The key of this model relies on the MTPC scheme, which encodes the interaural time difference (ITD) cues into spike patterns. This scheme naturally follows the functional structures of the human auditory localization system, rather than artificially computing of time difference of arrival. Besides, it highlights the advantages of SNN, such as event-driven and power efficiency. The MTPC is pipelined with two different SNN architectures, the convolutional SNN and recurrent SNN, by which it shows the applicability to various SNNs. This proposal is evaluated by the microphone collected location-dependent acoustic data, in a real-world environment with noise, obstruction, reflection, or other affects. The experiment results show a mean error azimuth of 1~3 degrees, which surpasses the accuracy of the other biologically plausible neuromorphic approach for sound source localization.

Related papers

• Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
  In neuromorphic computing, spiking neural networks (SNNs) perform inference tasks, offering significant efficiency gains for workloads involving sequential data. Recent advances in hardware and software have demonstrated that embedding a few bits of payload in each spike exchanged between the spiking neurons can further enhance inference accuracy. This paper investigates a wireless neuromorphic split computing architecture employing multi-level SNNs.
  arXiv  Detail & Related papers  (2024-11-07T14:08:35Z)
• Accurate Mapping of RNNs on Neuromorphic Hardware with Adaptive Spiking Neurons [2.9410174624086025]
  We present a $SigmaDelta$-low-pass RNN (lpRNN) for mapping rate-based RNNs to spiking neural networks (SNNs) An adaptive spiking neuron model encodes signals using $SigmaDelta$-modulation and enables precise mapping. We demonstrate the implementation of the lpRNN on Intel's neuromorphic research chip Loihi.
  arXiv  Detail & Related papers  (2024-07-18T14:06:07Z)
• Dual input neural networks for positional sound source localization [19.07039703121673]
  We introduce Dual Input Neural Networks (DI-NNs) as a simple and effective way to model these two data types in a neural network. We train and evaluate our proposed DI-NN on scenarios of varying difficulty and realism and compare it against an alternative architecture. Our results show that the DI-NN significantly outperforms the baselines, achieving a five times lower localization error than the LS method and two times lower than the CRNN in a test dataset of real recordings.
  arXiv  Detail & Related papers  (2023-08-08T09:59:56Z)
• Time-Parameterized Convolutional Neural Networks for Irregularly Sampled Time Series [26.77596449192451]
  Irregularly sampled time series are ubiquitous in several application domains, leading to sparse, not fully-observed and non-aligned observations. Standard sequential neural networks (RNNs) and convolutional neural networks (CNNs) consider regular spacing between observation times, posing significant challenges to irregular time series modeling. We parameterize convolutional layers by employing time-explicitly irregular kernels.
  arXiv  Detail & Related papers  (2023-08-06T21:10:30Z)
• Bayesian Neural Network Language Modeling for Speech Recognition [59.681758762712754]
  State-of-the-art neural network language models (NNLMs) represented by long short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly complex. In this paper, an overarching full Bayesian learning framework is proposed to account for the underlying uncertainty in LSTM-RNN and Transformer LMs.
  arXiv  Detail & Related papers  (2022-08-28T17:50:19Z)
• Time-Frequency Localization Using Deep Convolutional Maxout Neural Network in Persian Speech Recognition [0.0]
  Time-frequency flexibility in some mammals' auditory neurons system improves recognition performance. This paper proposes a CNN-based structure for time-frequency localization of audio signal information in the ASR acoustic model. The average recognition score of TFCMNN models is about 1.6% higher than the average of conventional models.
  arXiv  Detail & Related papers  (2021-08-09T05:46:58Z)
• PILOT: Introducing Transformers for Probabilistic Sound Event Localization [107.78964411642401]
  This paper introduces a novel transformer-based sound event localization framework, where temporal dependencies in the received multi-channel audio signals are captured via self-attention mechanisms. The framework is evaluated on three publicly available multi-source sound event localization datasets and compared against state-of-the-art methods in terms of localization error and event detection accuracy.
  arXiv  Detail & Related papers  (2021-06-07T18:29:19Z)
• Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks [61.76338096980383]
  A range of neural architecture search (NAS) techniques are used to automatically learn two types of hyper- parameters of state-of-the-art factored time delay neural networks (TDNNs) These include the DARTS method integrating architecture selection with lattice-free MMI (LF-MMI) TDNN training. Experiments conducted on a 300-hour Switchboard corpus suggest the auto-configured systems consistently outperform the baseline LF-MMI TDNN systems.
  arXiv  Detail & Related papers  (2020-07-17T08:32:11Z)
• Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks [55.0627904986664]
  Spiking Neural Networks (SNNs) usetemporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-power event-driven neuromorphic implementation. This paper investigates the contribution of spike timing dynamics to information encoding, synaptic plasticity and decision making, providing a new perspective to design of future DeepSNNs and neuromorphic hardware systems.
  arXiv  Detail & Related papers  (2020-03-26T11:13:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.