Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2409.00552

Digit Recognition using Multimodal Spiking Neural Networks

William Bjorndahl·Jack Easton·Austin Modoff·Eric C. Larson·Joseph Camp·Prasanna Rangarajan
View original source ↗Read source document ↗Export metadata ↓

Abstract

Spiking neural networks (SNNs) are the third generation of neural networks that are biologically inspired to process data in a fashion that emulates the exchange of signals in the brain. Within the Computer Vision community SNNs have garnered significant attention due in large part to the availability of event-based sensors that produce a spatially resolved spike train in response to changes in scene radiance. SNNs are used to process event-based data due to their neuromorphic nature. The proposed work examines the neuromorphic advantage of fusing multiple sensory inputs in classification tasks. Specifically we study the performance of a SNN in digit classification by passing in a visual modality branch (Neuromorphic-MNIST [N-MNIST]) and an auditory modality branch (Spiking Heidelberg Digits [SHD]) from datasets that were created using event-based sensors to generate a series of time-dependent events. It is observed that multi-modal SNNs outperform unimodal visual and unimodal auditory SNNs. Furthermore, it is observed that the process of sensory fusion is insensitive to the depth at which the visual and auditory branches are combined. This work achieves a 98.43% accuracy on the combined N-MNIST and SHD dataset using a multimodal SNN that concatenates the visual and auditory branches at a late depth.

Explore related subjects

eess.AScs.CVcs.MMcs.SD

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

William Bjorndahl, Jack Easton, Austin Modoff, Eric C. Larson, Joseph Camp, Prasanna Rangarajan. 2024-08-31. Digit Recognition using Multimodal Spiking Neural Networks. https://arxiv.org/abs/2409.00552

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Recovering the Zipfian Distribution in Unsupervised Term Discovery

Unsupervised term discovery involves segmenting unlabelled speech into word- or syllable-like units and clustering these into a lexicon of candidate types. True lexicons follow a Zipfian distribution, yet the dominant centre-based clustering approach -- K-means -- produces a more uniform distribution due to an inductive bias toward spherical clusters. In this paper we revisit graph-based clustering as a bottom-up alternative, where segment embeddings are connected by pairwise similarity and partitioned using the Leiden algorithm. We show that graph clustering substantially outperforms centre-based approaches (K-means, GMM, BIRCH) in both word- and syllable-level lexicon discovery across three languages, producing more Zipf-like distributions. Another bottom-up approach, agglomerative clustering with average linkage, also performs well, although it is computationally less efficient and allows for less control over the resulting distribution. Our work calls into question the dominance of centre-based clustering for term discovery, and promotes graph clustering as an attractive alternative.

eess.AS↗
arXiv2026

Preserving Speech-to-Text LLM Capabilities in Speech-to-Speech Generation

Strong speech-to-text (S2T) LLMs already provide robust speech perception and text reasoning, but adding speech-to-speech (S2S) output is challenging: fine-tuning the backbone can degrade the original S2T performance, while attaching a downstream talker reintroduces a serial text-to-speech bottleneck. We present PRIME-Speech, a frozen-backbone S2S conversion framework that trains only speech-generation modules. PRIME-Speech synchronizes a causal audio post-decoder with intermediate hidden states of the frozen backbone, so codec tokens are generated from the model's evolving reasoning trajectory rather than from completed text chunks. The post-decoder uses mixed hidden-state, text, and audio-history conditioning, and a training-time packing strategy with turn-level audio KV-cache and position reset stabilizes multi-turn spoken interaction without additional multi-turn S2S training data. Multi-token prediction further reduces the effective codec prediction rate and improves first-audio latency without modifying the reasoning path. Across speech translation, spoken QA, speech understanding, and multi-turn dialogue, PRIME-Speech preserves the S2T behavior of the frozen backbone while producing accurate, low-WER spoken responses.

eess.AS↗
arXiv2026

SRF-SVB: Style-Consistent Singing Voice Beautifying via Rectified Flow

Singing voice beautifying (SVB) aims to correct pitch and rhythm of amateur singing while enhancing vocal quality, preserving lyrics and the singer's timbre. Existing methods, however, suffer from limited generation quality and efficiency, and tend to neglect the preservation of the singer's style. We propose SRF-SVB, a style-consistent model for SVB via rectified flow, which achieves high-fidelity and efficient beautification covering pitch and rhythm correction. Furthermore, we design a context-guided masked mel-spectrogram inpainting mechanism that effectively preserves the amateur singer's style, including unique timbre and expressive patterns. Experiments on both English and Chinese test sets show that SRF-SVB outperforms baseline models in most objective and subjective metrics.

eess.AS↗