Low-power SNN-based audio source localisation using a Hilbert Transform spike encoding scheme

• URL: http://arxiv.org/abs/2402.11748v3
• Date: Tue, 03 Dec 2024 06:23:38 GMT
• Title: Low-power SNN-based audio source localisation using a Hilbert Transform spike encoding scheme
• Authors: Saeid Haghighatshoar, Dylan R Muir,
• Abstract summary: Sound source localisation is used in many consumer devices, to isolate audio from individual speakers and reject noise.<n>Dense band-pass filters are often needed to obtain narrowband signal components from wideband audio.<n>We demonstrate a novel method for sound source localisation on arbitrary microphone arrays, designed for efficient implementation in ultra-low-power spiking neural networks (SNNs)<n>Our approach achieves state-of-the-art accuracy for SNN methods, comparable with traditional non-SNN super-resolution beamforming.
• Score: 4.49657690895714
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Sound source localisation is used in many consumer devices, to isolate audio from individual speakers and reject noise. Localization is frequently accomplished by ``beamforming'', which combines phase-shifted audio streams to increase power from chosen source directions, under a known microphone array geometry. Dense band-pass filters are often needed to obtain narrowband signal components from wideband audio. These approaches achieve high accuracy, but narrowband beamforming is computationally demanding, and not ideal for low-power IoT devices. We demonstrate a novel method for sound source localisation on arbitrary microphone arrays, designed for efficient implementation in ultra-low-power spiking neural networks (SNNs). We use a Hilbert transform to avoid dense band-pass filters, and introduce a new event-based encoding method that captures the phase of the complex analytic signal. Our approach achieves state-of-the-art accuracy for SNN methods, comparable with traditional non-SNN super-resolution beamforming. We deploy our method to low-power SNN inference hardware, with much lower power consumption than super-resolution methods. We demonstrate that signal processing approaches co-designed with spiking neural network implementations can achieve much improved power efficiency. Our new Hilbert-transform-based method for beamforming can also improve the efficiency of traditional DSP-based signal processing.

Related papers

• Neural Network-Based Frequency Optimization for Superconducting Quantum Chips [3.4802501242383146]
  We propose a neural network-based frequency configuration approach for superconducting quantum chips. A trained neural network model estimates frequency configuration errors, and an intermediate optimization strategy identifies optimal configurations within localized regions of the chip. We also design a crosstalk-aware hardware-efficient ansatz for variational quantum eigensolvers, achieving improved energy computations.
  arXiv  Detail & Related papers  (2024-12-02T06:42:03Z)
• Power-Efficient Indoor Localization Using Adaptive Channel-aware Ultra-wideband DL-TDOA [7.306334571814026]
  We propose and implement a novel low-power channel-aware dynamic frequency DL-TDOA ranging algorithm. It comprises NLOS probability predictor based on a convolutional neural network (CNN), a dynamic ranging frequency control module, and an IMU sensor-based ranging filter.
  arXiv  Detail & Related papers  (2024-02-16T09:04:04Z)
• 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)
• Gradient-descent hardware-aware training and deployment for mixed-signal Neuromorphic processors [2.812395851874055]
  Mixed-signal neuromorphic processors provide extremely low-power operation for edge inference workloads. We demonstrate a novel methodology for ofDine training and deployment of spiking neural networks (SNNs) to the mixed-signal neuromorphic processor DYNAP-SE2.
  arXiv  Detail & Related papers  (2023-03-14T08:56:54Z)
• Spiking Neural Network Decision Feedback Equalization [70.3497683558609]
  We propose an SNN-based equalizer with a feedback structure akin to the decision feedback equalizer (DFE) We show that our approach clearly outperforms conventional linear equalizers for three different exemplary channels. The proposed SNN with a decision feedback structure enables the path to competitive energy-efficient transceivers.
  arXiv  Detail & Related papers  (2022-11-09T09:19:15Z)
• Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks [101.59367762974371]
  In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments. It is difficult to train a deep neural network (DNN) with limited pilot signals, hindering its practical applications. We design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm.
  arXiv  Detail & Related papers  (2022-10-08T04:32:58Z)
• Simple Pooling Front-ends For Efficient Audio Classification [56.59107110017436]
  We show that eliminating the temporal redundancy in the input audio features could be an effective approach for efficient audio classification. We propose a family of simple pooling front-ends (SimPFs) which use simple non-parametric pooling operations to reduce the redundant information. SimPFs can achieve a reduction in more than half of the number of floating point operations for off-the-shelf audio neural networks.
  arXiv  Detail & Related papers  (2022-10-03T14:00:41Z)
• Sub-mW Neuromorphic SNN audio processing applications with Rockpool and Xylo [0.0]
  Spiking Neural Networks (SNNs) provide an efficient computational mechanism for temporal signal processing. SNNs have been historically difficult to configure, lacking a general method for finding solutions for arbitrary tasks. Here we demonstrate a convenient high-level pipeline to design, train and deploy arbitrary temporal signal processing applications to sub-mW SNN inference hardware.
  arXiv  Detail & Related papers  (2022-08-27T11:50:32Z)
• Efficient spike encoding algorithms for neuromorphic speech recognition [5.182266520875928]
  Spiking Neural Networks (SNN) are very effective for neuromorphic processor implementations. Real-valued signals are encoded as real-valued signals that are not well-suited to SNN. In this paper, we study four spike encoding methods in the context of a speaker independent digit classification system.
  arXiv  Detail & Related papers  (2022-07-14T17:22:07Z)
• Optimal quantum control via genetic algorithms for quantum state engineering in driven-resonator mediated networks [68.8204255655161]
  We employ a machine learning-enabled approach to quantum state engineering based on evolutionary algorithms. We consider a network of qubits -- encoded in the states of artificial atoms with no direct coupling -- interacting via a common single-mode driven microwave resonator. We observe high quantum fidelities and resilience to noise, despite the algorithm being trained in the ideal noise-free setting.
  arXiv  Detail & Related papers  (2022-06-29T14:34:00Z)
• Acoustic-Net: A Novel Neural Network for Sound Localization and Quantification [28.670240455952317]
  A novel neural network, termed the Acoustic-Net, is proposed to locate and quantify the sound source simply using the original signals. The experiments demonstrate that the proposed method significantly improves the accuracy of sound source prediction and the computing speed.
  arXiv  Detail & Related papers  (2022-03-31T12:20:09Z)
• Frequency-bin entanglement from domain-engineered down-conversion [101.18253437732933]
  We present a single-pass source of discrete frequency-bin entanglement which does not use filtering or a resonant cavity. We use a domain-engineered nonlinear crystal to generate an eight-mode frequency-bin entangled source at telecommunication wavelengths.
  arXiv  Detail & Related papers  (2022-01-18T19:00:29Z)
• An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic [72.35307086274912]
  High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices. We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations. Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
  arXiv  Detail & Related papers  (2022-01-09T09:31:50Z)
• Mixed Precision Low-bit Quantization of Neural Network Language Models for Speech Recognition [67.95996816744251]
  State-of-the-art language models (LMs) represented by long-short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming increasingly complex and expensive for practical applications. Current quantization methods are based on uniform precision and fail to account for the varying performance sensitivity at different parts of LMs to quantization errors. Novel mixed precision neural network LM quantization methods are proposed in this paper.
  arXiv  Detail & Related papers  (2021-11-29T12:24:02Z)
• Neural Calibration for Scalable Beamforming in FDD Massive MIMO with Implicit Channel Estimation [10.775558382613077]
  Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. We propose a deep learning-based approach that directly optimize the beamformers at the base station according to the received uplink pilots. A neural calibration method is proposed to improve the scalability of the end-to-end design.
  arXiv  Detail & Related papers  (2021-08-03T14:26:14Z)
• Quantized Neural Networks via {-1, +1} Encoding Decomposition and Acceleration [83.84684675841167]
  We propose a novel encoding scheme using -1, +1 to decompose quantized neural networks (QNNs) into multi-branch binary networks. We validate the effectiveness of our method on large-scale image classification, object detection, and semantic segmentation tasks.
  arXiv  Detail & Related papers  (2021-06-18T03:11:15Z)
• SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network [79.04274563889548]
  We propose SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples. We introduce a worst-case learning threshold for comparing the results of our network relative to the underlying data distributions. In simulation, we find that our algorithm is always able to surpass the threshold for three-bit data but often cannot exceed the threshold for one-bit data.
  arXiv  Detail & Related papers  (2021-06-10T04:21:20Z)
• Deep Networks for Direction-of-Arrival Estimation in Low SNR [89.45026632977456]
  We introduce a Convolutional Neural Network (CNN) that is trained from mutli-channel data of the true array manifold matrix. We train a CNN in the low-SNR regime to predict DoAs across all SNRs. Our robust solution can be applied in several fields, ranging from wireless array sensors to acoustic microphones or sonars.
  arXiv  Detail & Related papers  (2020-11-17T12:52:18Z)
• Using deep learning to understand and mitigate the qubit noise environment [0.0]
  We propose to address the challenge of extracting accurate noise spectra from time-dynamics measurements on qubits. We demonstrate a neural network based methodology that allows for extraction of the noise spectrum associated with any qubit surrounded by an arbitrary bath. Our results can be applied to a wide range of qubit platforms and provide a framework for improving qubit performance.
  arXiv  Detail & Related papers  (2020-05-03T17:13:14Z)

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.