Spike-based Neuromorphic Model for Sound Source Localization
Authors: Dehao Zhang, Shuai Wang, Ammar Belatreche, Wenjie Wei, Yichen Xiao, Haorui Zheng, Zijian Zhou, Malu Zhang, Yang Yang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimentation demonstrates that our SSL framework achieves state-of-the-art accuracy in SSL tasks. Furthermore, it shows exceptional noise robustness and maintains high accuracy even at very low signal-to-noise ratios. |
| Researcher Affiliation | Academia | 1 University of Electronic Science and Technology of China 2 Northumbria University 3 Peking University |
| Pseudocode | No | The paper describes methods using mathematical equations and text, but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | Additionally, our code will be made available on subsequent after review. |
| Open Datasets | Yes | In this section, we evaluate our proposed spike-based SSL framework performance on three datasets: the HRTF [57], Single Words [30], and SLo Clas dataset [42]. |
| Dataset Splits | No | The paper states it evaluates performance on datasets (HRTF, Single Words, SLo Clas) and describes metrics like MAE and accuracy with specific η values, but it does not explicitly provide the training, validation, and test dataset splits by percentage, sample counts, or reference to predefined splits in the text. |
| Hardware Specification | No | The paper discusses theoretical energy estimations using a 45nm technology assumption, but does not specify the actual hardware (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific version numbers for software dependencies such as programming languages, libraries, or frameworks (e.g., Python version, PyTorch version). |
| Experiment Setup | Yes | Table 3: Experimental configuration of the sound localization task. Attributes Setup 1. Data preprocessing: Sampling rate (Hz) 16000 Frame length (ms) 170 Frame stride (ms) 170 RF neurons n 512 Number of Microphones 4 2. RF-PLC setting: CQT frequency range (Hz) [0, 8800] τ (ms) 0.0625 Frequency channels N 40 Coincidence detector Nτ 51 Microphone pairs C 6 3. SNN Hyperparameter: α 0.75 Timestep 4 Epochs 300 Batch size 128 Optimizer Adam Base learning rate 1e-3 Learning rate decay Cosine Weight decay 5e-3 |