# Comparison of Hardware-friendly, Audio-to-spikes Cochlear Encoding for Neuromorphic Processing

Valentin Meunier, Amélie Gruel, Adrien F Vincent, Sylvain Saïghi

► To cite this version:

Valentin Meunier, Amélie Gruel, Adrien F Vincent, Sylvain Saïghi. Comparison of Hardware-friendly, Audio-to-spikes Cochlear Encoding for Neuromorphic Processing. 2025 IEEE 7th International Conference on AI Circuits and Systems (AICAS), Apr 2025, Bordeaux, France. pp.1-5, 10.1109/AICAS64808.2025.11173106. hal-05344616

HAL Id: hal-05344616

https://hal.science/hal-05344616v1

Submitted on 3 Nov 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>
# Comparison of Hardware-friendly, Audio-to-spikes Cochlear Encoding for Neuromorphic Processing

Valentin Meunier*, Amélie Gruel*, Adrien F. Vincent*, and Sylvain Saïghi*

* Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, F-33400 Talence, France

Email: {valentin.meunier, amelie.gruel, adrien.vincent, sylvain.saighi}@ims-bordeaux.fr

***Abstract***—Obtaining data from neuromorphic sensors and processing it with Spiking Neural Networks (SNNs) is a promising solution to lower energy consumption in Artificial Intelligence systems. The current rarity of natively neuromorphic datasets promotes the development of software tools to translate input sensory data into spikes. However, highly bio-mimetic simulators can be challenging to implement on digital hardware. In this work, we evaluate the impact of audio-to-spikes encoding on an audio classification task regarding its accuracy, energy efficiency, and ease of hardware implementation. We compare a state-of-the-art, bio-mimetic cochlear encoder to a lighter and more hardware-friendly model, on the benchmark datasets Heidelberg Digits and Google Speech Commands. Our work shows that the latter model can reach higher accuracy and energy efficiency, paving the way for better neuromorphic systems.

***Index Terms***—spike encoding, spiking neural networks, audio processing, artificial cochlea, neuromorphic systems.

## I. INTRODUCTION

Neuromorphic computing is promising for processing latency, data storage, and energy consumption. Neuromorphic sensors allow to record and process sensory data in spiked sequences [1], [2]. This technology led to the production of various event-based datasets such as N-MNIST [3], DVS-Gesture [4], GEN1 [5], etc. — but such datasets remain rare compared to the ones available for traditional Artificial Intelligence. To overcome this issue, software simulators emulating the behavior of a neuromorphic sensor have been developed to obtain simili spiked datasets, e.g. targeting video inputs [6]–[8]. Similarly, the Spiking Heidelberg Digits (SHD) [9] dataset and the Spiking Speech Commands (SSC) [9] dataset are known benchmarks for neuromorphic speech recognition. These two datasets for audio-based classification were obtained from existing traditional audio recordings processed by Lauscher [9], an artificial cochlea model that reproduces the human’s inner ear and parts of the ascending auditory pathway. Despite being extensively used within the state of the art [10]–[15], SHD and SSC suffer from Lauscher’s limits: such a highly bio-mimetic encoder is difficult to implement in digital hardware due to its intrinsic mathematical complexity and high memory demands, especially for embedded targets. Can also be mentioned the Speech2Spikes audio processing

pipeline [16], which consists in a Sliding Discrete Fourier Transform mapped onto a set of Mel-frequency bands, as well as the Spiking-Leaf method [17], composed of a learnable filter bank and a novel spiking neuron model. While both approaches address Lauscher’s limits, their encoding methods are computationally demanding, leading to significant energy consumption from the CPU which could offset any efficiency gains of a neuromorphic system. On the contrary, an encoder that remains both bio-inspired and easy to implement in digital hardware is appealing to anticipate in the longer term the spread of natively event-based low-power bio-inspired sensors such as [18], while yet addressing shorter term needs.

In this work, we use a software implementation of such a less computationally intensive FPGA-based encoding architecture from [19], in order to investigate by simulation means the impact of audio-to-spikes encoding on the accuracy of an audio classification task and the corresponding energy efficiency. We compare the encoding of two audio datasets [9], [20] encoded in spikes using either Lauscher or our encoder.

## II. METHODS

### A. Overview and datasets

Fig. 1 introduces the overall simulation framework that we use for our study. First, a digital 1D audio signal (i.e. a time series) is encoded, by software means, into spikes using either a bio-mimetic or a bio-inspired, more hardware-friendly, encoder. Both solutions take inspiration from biological cochleas, and output spikes in $N_f$ different frequency channels. Those spikes are then processed by a dense recurrent SNN, with $N_f$ inputs, a single hidden layer with $N_h = 200$ units, and $N_o$ outputs, where $N_o$ is the number of classes in the dataset under study. Subsections II-B and II-C provide more details on cochlear encoding and the recurrent SNN classifier respectively. In this paper, we consider two audio datasets, the Heidelberg Digits (HD) [9] and Google Speech Commands

<figure><img src="image_2.png" /><figcaption>Fig. 1: Processing framework overview.</figcaption></figure>

This work is supported by a public grant overseen by the French National Research Agency (ANR) as part of the ‘Chaires IA’ programme (GrAI project ANR-19-CHIA-0003) and as part of the ‘PEPR IA France 2030’ programme (Emergences project ANR-23-PEIA-0002).
(GSC) [20], converted into spikes using two different encoding models (see numerical details in Table I). The conversion of audio data into spikes from HD to SHD and from GSC to SSC was done by the bio-mimetic encoder Lauscher using $N_f = 700$ filters. This encoding emulates closely the auditory system behaviour, using the physical equation of vibration propagation along the cochlear membrane. However, it is computationally demanding and energy intensive. Optimizing it for peculiar needs is also challenging for the end-user as the impact of encoding parameters on the subsequent spikes can be intricate. We use a bio-inspired encoding that is both more computationally-friendly and allows for an easier tuning with parameters that more directly shape the outline of output spikes density. To prevent confusion, we refer to the datasets obtained by our encoding of the HD dataset into spikes as “Neuromorphic Heidelberg Digits” (NHD) and of the GSC dataset into spikes as “Neuromorphic Speech Commands” (NSC). This encoding is done according to different values of parameters and $N_f$ filters, as presented in Fig. 1, to create variants of NHD and NSC. SHD, SSC, and these variants of NHD and NSC are then processed by an SNN classifier and evaluated according to different benchmark metrics.

## B. Cochlear encoding

In this work, we propose to encode HD and GSC into spikes with only $N_f = 13$ band-pass filters, logarithmically spaced [21] within the human hearing range [22]. More specifically, the lowest filter has its central frequency set at 25 Hz for both HD and GSC, while the one of the highest filter is set at 6650 Hz for HD, and at 16,667 Hz for GSC, due to their different sampling rates. The choice of $N_f = 13$ filters rather than 700 like in [9] draws inspiration from the Mel-Frequency Cepstral Coefficients (MFCC) [23], a common audio representation using coefficients typically set to 13 for feature extraction. The first objective of this work is to confirm that 13 filters are sufficient to extract distinct patterns of spike density. Indeed, using only 13 filters in the encoding, i.e. 13 pairs of filters and neurons within the input layer of a subsequent SNN model, leads to a reduced energy consumption during data encoding as well as a smaller number of synapses and synaptic events within the model, thus proportionally reducing energy consumption [24], [25]. Finally, it allows for an easier hardware implementation by requiring less resources.

In addition to the amount of filters, the hardware-friendly audio-to-s spikes encoding framework under study requires the setting of three additional parameters that shapes the output spike density, which are (with their tuning values):

* The threshold $T$, i.e. the value the membrane potential must exceed for the neuron to send a spike. Its tuning values are $\{0.1, 0.3, 1, 3, 10\}$, in arbitrary units.

* The decay rate $D$, which indicates the fraction by which the membrane potential is reduced at each time step. Its effect thus depend on the audio sample rate. Its tuning values are $\{0.01, 0.03, 0.1, 0.3, 1\}$.

<table><caption>TABLE I: Statistics of the datasets under study.</caption><thead><tr><th></th><th>SHD</th><th>NHD</th><th>SSC</th><th>NSC</th></tr></thead><tbody><tr><td>Number of audio samples</td><td>10,420</td><td></td><td>105,830</td><td></td></tr><tr><td>Number of classes</td><td>20</td><td></td><td>35</td><td></td></tr><tr><td>Train/Valid/Test split (in %)</td><td>78/-/22</td><td>80/-/20</td><td>71/10/19</td><td>72/9/19</td></tr></tbody></table>

<table><caption>TABLE II: Classification performance for HD and GSC with different encoding and parameters at epoch 200.</caption><thead><tr><th rowspan="2">Data</th><th rowspan="2">Variant<br>abbreviation</th><th colspan="3">Parameters</th><th rowspan="2">Metric</th><th colspan="2">Performance</th></tr><tr><th>Q</th><th>D</th><th>T</th><th>N<sub>f</sub> = 13</th><th>N<sub>f</sub> = 700</th></tr></thead><tbody><tr><td rowspan="3">SHD<br>(Lauscher)</td><td rowspan="3">SHD</td><td rowspan="3"></td><td rowspan="3"></td><td rowspan="3"></td><td>Acc</td><td colspan="2"></td></tr><tr><td>Eff<sub>tr</sub></td><td colspan="2">66.08%</td></tr><tr><td>Eff<sub>inf</sub></td><td colspan="2">4.69 × 10<sup>-11</sup><br>3.29 × 10<sup>-8</sup></td></tr><tr><td rowspan="6">NHD<br>(Ours)</td><td rowspan="3">NHD{N<sub>f</sub>}<sub>_A</sub></td><td rowspan="3">2.5</td><td rowspan="3">0.01</td><td rowspan="3">3</td><td>Acc</td><td colspan="2">92.08%</td></tr><tr><td>Eff<sub>tr</sub></td><td>2.12 × 10<sup>-10</sup></td><td>5.74 × 10<sup>-12</sup></td></tr><tr><td>Eff<sub>inf</sub></td><td>1.86 × 10<sup>-7</sup></td><td>4.64 × 10<sup>-9</sup></td></tr><tr><td rowspan="3">NHD{N<sub>f</sub>}<sub>_B</sub></td><td rowspan="3">2.5</td><td rowspan="3">0.01</td><td rowspan="3">10</td><td>Acc</td><td colspan="2">81.18%</td></tr><tr><td>Eff<sub>tr</sub></td><td>5.12 × 10<sup>-10</sup></td><td>3.72 × 10<sup>-11</sup></td></tr><tr><td>Eff<sub>inf</sub></td><td>3.37 × 10<sup>-7</sup></td><td>3.02 × 10<sup>-8</sup></td></tr><tr><td rowspan="3">SSC<br>(Lauscher)</td><td rowspan="3">SSC</td><td rowspan="3"></td><td rowspan="3"></td><td rowspan="3"></td><td>Acc</td><td colspan="2">35.98%</td></tr><tr><td>Eff<sub>tr</sub></td><td colspan="2">1.89 × 10<sup>-12</sup></td></tr><tr><td>Eff<sub>inf</sub></td><td colspan="2">1.77 × 10<sup>-9</sup></td></tr><tr><td rowspan="3">NSC<br>(Ours)</td><td rowspan="3">NSC{N<sub>f</sub>}<sub>_AB</sub></td><td rowspan="3">2.5</td><td rowspan="3">0.01</td><td rowspan="3">3</td><td>Acc</td><td colspan="2">58.26%</td></tr><tr><td>Eff<sub>tr</sub></td><td>2.63 × 10<sup>-11</sup></td><td>1.10 × 10<sup>-12</sup></td></tr><tr><td>Eff<sub>inf</sub></td><td>1.55 × 10<sup>-8</sup></td><td>8.37 × 10<sup>-10</sup></td></tr></tbody></table>

* The quality factor $Q$, which sets the bandwidth of the fourth-order Butterworth band-pass filters [26] used by our cochlear encoding. Its tuning values are $\{2.5, 5, 10\}$.

Each trio of parameter values leads to the creation of a neuromorphic variant of the original non-spiking dataset, which is then studied during the tuning phase. Thus are produced 75 variants of NHD and 75 variants of NSC.

## C. SNN classifier

The SNN classifier introduced in the online tutorial for the SHD [9] is used as benchmark in this work with limited modifications. It is composed of Leaky Integrate-and-Fire (LIF) [27] neurons and is trained with surrogate gradients and backpropagation through time (BPTT), using supervised loss functions. Illustrated in Fig. 1, its architecture is quite simple: one input layer of $N_f$ units, one hidden layer of $N_h$ units and one output layer of $N_o$ units. $N_f$ corresponds to the number of filters used by the cochlear encoding; [9] uses $N_f = 700$, whereas we adapt it to $N_f = 13$ in this work. $N_o$ corresponds to the number of classes within the dataset, either 20 or 35 (see Table I). As in the online tutorial used for reference [9], $N_h$ is set to 200 and no validation phase is used.

We choose this SNN classifier as benchmark due to its simplicity even though it does not achieve state-of-the-art results [10], [11]: indeed, the focus of our study is on the impact of the audio-to-spike encoding on the aforementioned metrics rather than on optimizing the downstream performance.

We optimise the classifier’s software implementation to reduce memory consumption and record values required for the evaluation of the datasets and their variants, such as the number of synaptic events at each layer within the model. Indeed, the quantity of spikes is fixed for SHD and SSC, but consequently varies for the NHD and NSC variants depending on the tuning of the parameters of our encoding model. The results presented in this work are averaged over five simulations initialised with different fixed seeds, for initial weight reproducibility. Are recorded: the number of synaptic
<figure><img src="image_3.png" /><figcaption>Fig. 2: Hyperparameter maps at epoch 150 for NHD (left) and NSC (right) averaged per sample and evaluated according to the accuracy (first row), number of training spikes (second row), and training and inference efficiency (respectively third and last rows). White corresponds to parameter combinations for which our cochlear encoding does not produce spikes. Grey crosses indicate parameter combinations producing less than 10 spikes per sample, too low to allow for accurate classification. The black line delimits the regions of parameters for which the classification accuracy overcomes (on the left) or not (on the right) the one obtained on the reference datasets (that is, 66.08% on SHD and 35.98% on SSC).</figcaption></figure>

events occurring within the SNN classifier during inference and training; the inference accuracy *Acc*, which evaluates the performance of classification on the test subsets; the efficiency during training *Eff*<sub>tr</sub> and during inference *Eff*<sub>inf</sub>, respectively calculated as the quotient of the test accuracy by the number of spikes within the SNN classifier, either accumulated during training phases up until the last epoch (*Eff*<sub>tr</sub>) or only during the last epoch's inference phase (*Eff*<sub>inf</sub>). These efficiency metrics aim to evaluate the performance conditioned by the corresponding energy cost required from the SNN classifier, either to reach this performance or once learning is done.

### D. *Selection of dataset variants*

In the first round of simulations (i.e. the parameter tuning phase), the SNN classifier is applied to each 75 variants of NHD and 75 variants of NSC. The corresponding results (displayed in Fig. 2) allow us to select the best variants of NHD and NSC produced using $N_f = 13$ filters, in relation to the three metrics *Acc*, *Eff*<sub>tr</sub> and *Eff*<sub>inf</sub>. We discard variants leading to less than 10 spikes per sample; additionally, we only consider the variants leading to a classification accuracy overcoming the reference performance. The parameter values leading to each “best” variant as well as its corresponding

abbreviated appellation are introduced in Tab. II. For example, the 13-channel variant of NHD reaching the highest classification accuracy *Acc* is renamed “NHD13_A”.

Secondly, to allow for a fairer and stricter comparison with state-of-the-art, we produce NHD and NSC variants with similar encoding parameters but using a larger number of filters $N_f = 700$, on which we assess the SNN classification performance (over 200 epochs as in [9]).

## III. SIMULATION RESULTS

As previously stated, Fig. 2 shows the NHD and NSC hyperparameter maps related to the three metrics *Acc*, *Eff*<sub>tr</sub> and *Eff*<sub>inf</sub> as well as the number of spikes accumulated during the SNN classifier training. These results lead to two key conclusions. Firstly, 13 filters are indeed enough to efficiently encode audio into spikes: some 13-channel variants reach a performance similar to (NHD) or even outperforms (NSC) their 700-channel counterparts, as shown by classifications results (*Acc*) in Table II. Secondly, the information provided within spikes are heavily influenced by the cochlear encoding parameters: a significant subset of parameters lead to an absence of spikes in the variant. When spikes are present, the quality of the information relayed can vary greatly; for
<figure><img src="image_4.png" /><figcaption>Fig. 3: Inference accuracy over 200 epochs for (a) SHD and NHD variants and (b) SSC and NSC variants.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 4: Inference efficiency over 200 epochs for (a) SHD and NHD variants and (b) SSC and NSC variants.</figcaption></figure>

example, NHD's *Acc* ranges from almost 0% to 92%. As the parameter sub-space leading to higher accuracy do not necessarily overlap with the sub-space with higher efficiency, Fig. 2 allows to select parameters values that optimize energy cost for a given accuracy level.

Fig. 3 presents the evolution of *Acc* during training, for SHD, SSC, and NHD and NSC variants with 13 or 700 filters. It shows that some 700-channel variants of NHD and NSC lead to an SNN classification outperforming SHD and SSC. Moreover, NHD13_A allows to reach similar accuracy as NHD700_A, while NSC13_A outperforms NSC700_A by 13 points. This confirms our initial hypothesis that reducing the number of filters can achieve similar SNN classification performance, and in some cases, even improve it.

Fig. 4 shows the evolution of $Eff_{inf}$ during training for the same input data as in Fig. 3. It highlights the fact that the efficiency of our encoding can come close to Lauscher with the same amount of filters (see NHD700_B). Moreover, variants with a lesser number of filters are systematically remarkably

<figure><img src="image_6.png" /><figcaption>Fig. 5: Train efficiency at epoch 200 for SHD and NHD variants (left) and SSC and NSC variants (right).</figcaption></figure>

more efficient.

Fig. 5 presents the values of $Eff_{tr}$ at epoch 200 for the same input data as in Fig. 3 and 4. It shows that NHD700_B allow for a trade-off between performance and cost of training similar to SHD. Additionally, all NHD13 and NSC13 variants are significatively more interesting than SHD and SSC considering $Eff_{tr}$. The reason why 700-channel variants underperform might be that the corresponding spiking profiles are too dense for the SNN classifier to efficiently recognise patterns, compared to data with a smaller spike density and more separable patterns. It is essential for a spiking dataset to optimise the trade-off between higher SNN classification performance and lower energy cost. As such, choosing NHD13_A and NSC13_AB, which outperform SHD and SSC respectively according to *Acc*, $Eff_{tr}$ and $Eff_{inf}$, is a finer choice than SHD and SSC.

## IV. CONCLUSION

In this work, we evaluate the impact of audio-to-spikes encoding on the overall classification performance and energy efficiency. We demonstrate that 1) encoding parameters heavily influence the statistics of output spikes; and 2) with good parameter tuning, the cochlear encoding heavily inspired from [19] allows to reach a significantly better classification accuracy than the more bio-mimetic approach of Lauscher [9], with a lower theoretical energy cost. The cochlear encoding derived from [19] leads to a similar (for HD) or better (for GSC) classification performance when reducing the number of filters used from $N_f = 700$ (a bio-inspired number, used by Lauscher) to $N_f = 13$. This reduced number of filters leads to a reduced number of synaptic events as well as to an easier downstream hardware implementation, due to the reduction of required memory usage and energy consumption. All in all, despite SHD and SSC omnipresence in the state of the art, the hardware-friendly encoder inspired by [19] seems to be a promising alternative for the future of neuromorphic audio processing.

## REFERENCES

[1] A. Vanarse, A. Osseiran, and A. Rassau, "A review of current neuromorphic approaches for vision, auditory, and olfactory sensors," *Frontiers in Neuroscience*, vol. 10, p. 115, 2016.

[2] Y. Yang, C. Bartolozzi, H. H. Zhang, and R. A. Nawrocki, "Neuro-morphic electronics for robotic perception, navigation and control: A survey," *Engineering Applications of Artificial Intelligence*, vol. 126, p. 106838, 2023.
[3] G. Orchard, A. Jayawant, G. K. Cohen, and N. Thakor, "Converting static image datasets to spiking neuromorphic datasets using saccades," *Frontiers in Neuroscience*, vol. 9, 2015.

[4] A. Amir, B. Taba, D. Berg, T. Melano, J. McKinstry, C. Di Nolfo, T. Nayak, A. Andreopoulos, G. Garreau, M. Mendoza, J. Kusnitz, M. Debole, S. Esser, T. Delbruck, M. Flickner, and D. Modha, "A low power, fully event-based gesture recognition system," in *2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)*, pp. 7388-7397, 2017.

[5] P. de Tournemire, D. Nitti, E. Perot, D. Migliore, and A. Sironi, "A large scale event-based detection dataset for automotive," *CoRR*, vol. abs/2001.08499, 2020.

[6] Y. Hu, S.-C. Liu, and T. Delbruck, "v2e: From video frames to realistic DVS events," in *Proceedings of the IEEE/CVF conference on computer vision and pattern recognition*, pp. 1312-1321, 2021.

[7] D. Gehrig, M. Gehrig, J. Hidalgo-Carrió, and D. Scaramuzza, "Video to events: Recycling video datasets for event cameras," in *IEEE Conf. Comput. Vis. Pattern Recogn. (CVPR)*, June 2020.

[8] Z. Zhang, S. Cui, K. Chai, H. Yu, S. Dasgupta, U. Mahbub, and T. Rahman, "V2CE: Video to continuous events simulator," 2024.

[9] B. Cramer, Y. Stradmann, J. Schemmel, and F. Zenke, "The Heidelberg spiking data sets for the systematic evaluation of spiking neural networks," *IEEE Transactions on Neural Networks and Learning Systems*, vol. 33, no. 7, pp. 2744-2757, 2022.

[10] M. Schöne, N. M. Sushima, J. Zhuge, C. Mayr, A. Subramoney, and D. Kappel, "Scalable event-by-event processing of neuromorphic sensory signals with deep state-space models," 2024.

[11] I. Hammouamri, I. Khalfaoui-Hassani, and T. Masquelier, "Learning delays in spiking neural networks using dilated convolutions with learnable spacings," 2023.

[12] P. Sun, Y. Chua, P. Devos, and D. Botteldooren, "Learnable axonal delay in spiking neural networks improves spoken word recognition," *Frontiers in Neuroscience*, vol. 17, 2023.

[13] C. Yu, Z. Gu, D. Li, G. Wang, A. Wang, and E. Li, "STSC-SNN: Spatio-temporal synaptic connection with temporal convolution and attention for spiking neural networks," *Frontiers in Neuroscience*, vol. 16, 2022.

[14] A. Bittar and P. N. Garner, "A surrogate gradient spiking baseline for speech command recognition," *Frontiers in Neuroscience*, vol. 16, 2022.

[15] M. Dampfhoffer, T. Mesquida, A. Valentian, and L. Anghel, "Investigating current-based and gating approaches for accurate and energy-efficient spiking recurrent neural networks," in *Artificial Neural Networks and Machine Learning – ICANN 2022 (E. Pimenidis, P. Angelov, C. Jayne, A. Papaleonidas, and M. Aydin, eds.)*, (Cham), pp. 359-370, Springer Nature Switzerland, 2022.

[16] K. M. Stewart, T. Shea, N. Pacik-Nelson, E. Gallo, and A. Danielescu, "Speech2Spikes: Efficient audio encoding pipeline for real-time neuromorphic systems," in *Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference*, NICE '23, (New York, NY, USA), p. 71-78, Association for Computing Machinery, 2023.

[17] Z. Song, J. Wu, M. Zhang, M. Z. Shou, and H. Li, "Spiking-Leaf: A learnable auditory front-end for spiking neural networks," in *ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, pp. 226-230, IEEE, 2024.

[18] V. Chan, S.-C. Liu, and A. van Schaik, "AER EAR: A matched silicon cochlea pair with address event representation interface," *IEEE Transactions on Circuits and Systems I: Regular Papers*, vol. 54, no. 1, pp. 48-59, 2007.

[19] C. Gillet, A. Vincent, B. Le Gal, and S. Saïghi, "A High-Level Methodology to Evaluate and Optimize Digital Architectures Targeting Spike Encoding," *IEEE Access*, vol. 11, pp. 120654-120665, 2023.

[20] P. Warden, "Speech commands: A dataset for limited-vocabulary speech recognition," 2018.

[21] D. S. Mistry and A. Kulkarni, "Overview: Speech recognition technology, mel-frequency cepstral coefficients (MFCC), artificial neural network (ANN)," *International Journal of Engineering Research and Technology*, vol. 2, no. 10, 2013.

[22] H. E. Heffner and R. S. Heffner, "Hearing ranges of laboratory animals," *Journal of the American Association for Laboratory Animal Science*, vol. 46, no. 1, pp. 20-22, 2007.

[23] L. N. M. and S. K. Kopparapu, "Choice of Mel filter bank in computing MFCC of a resampled speech," 2014.

[24] M. Dampfhoffer, T. Mesquida, A. Valentian, and L. Anghel, "Are SNNs really more energy-efficient than ANNs? an in-depth hardware-aware study," *IEEE Transactions on Emerging Topics in Computational Intelligence*, vol. 7, no. 3, pp. 731-741, 2023.

[25] M. Dampfhoffer, T. Mesquida, E. Hardy, A. Valentian, and L. Anghel, "Leveraging sparsity with spiking recurrent neural networks for energy-efficient keyword spotting," in *ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, pp. 1-5, 2023.

[26] M. Shouran and E. Elgamli, "Design and implementation of Butterworth filter," *International Journal of Innovative Research in Science Engineering and Technology*, vol. 9, no. 9, pp. 7975-7983, 2020.

[27] A. D. Rast, F. Galluppi, X. Jin, and S. Furber, "The leaky integrate-and-fire neuron: A platform for synaptic model exploration on the SpiNNaker chip," in *The 2010 International Joint Conference on Neural Networks (IJCNN)*, pp. 1-8, 2010.