Modulating STDP With Back-Propagated Error Signals to Train SNNs for Audio Classification
Bibliographic record
Abstract
Audio classification has many practical applications such as noise pollution detection, wildlife monitoring, speech recognition, and more. For many of these applications, deploying classifiers on low powered devices for persistent deployment is desirable. Artificial neural networks (ANN) have achieved state-of-the-art performance on audio classification tasks; however, it is not always feasible to deploy modern ANNs to embedded devices due to their high power consumption. Biologically inspired spiking neural networks (SNN) have been shown to significantly reduce power consumption during inference when compared with equivalent ANNs, and they have also been theoretically proven to be more computationally powerful than stateless ANNs. This work proposes an audio classification system using SNNs, and a learning algorithm is developed for classification with multilayer SNNs which combines biologically plausible spike-timing-dependent plasticity (STDP) with spatial error backpropagation. By allowing the STDP process to account for both temporal dependencies and the non-differentiable activation function derivative, the proposed learning rule successfully trains multilayer SNNs for the considered classification tasks. Through the STDP process, the proposed learning rule is also capable of online learning; explicit storage of values from previous timesteps is not required, in contrast to the widely adopted backpropagation through time (BPTT) algorithm. The proposed approach approaches the performance of SNNs trained via BPTT on the classification tasks. SNNs trained with the proposed learning rule are evaluated on the Iris Flower dataset, the Real-World Computing Partnership sounds dataset, the Free Spoken Digits Dataset, and the UrbanSound8k dataset.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".