Infrasound signal classification based on attention-CNN-BiLSTM

Abstract

Accurate classification of infrasound signals holds significant value for disaster early warning and military reconnaissance. To achieve precise recognition, this paper proposes an Attention-CNN-BiLSTM fusion method. The method first extracts energy aggregation features within local frequency bands of the infrasound signal power spectrum using a Convolutional Neural Network (CNN). Subsequently, it captures long-range contextual information through a Bidirectional Long Short-Term Memory (BiLSTM) network. Crucially, a Convolutional Block Attention Module (CBAM) is embedded to adaptively weight discriminative features, thereby enhancing focus on critical features. The experiments are conducted using the Comprehensive Nuclear-Test-Ban Treaty Organization historical infrasound data and publicly available infrasound datasets. Results demonstrate that the proposed Attention-CNN-BiLSTM classification model achieves an improvement of 6.97%–13.12% in classification accuracy on the proprietary dataset compared to four classical deep learning benchmarks: LeNet-5, AlexNet, ResNet, and LSTM. Furthermore, it attained a classification accuracy of 98.06% on the public dataset. These findings indicate that the method offers high classification accuracy and exhibits strong practical potential for infrasound signal classification.

Keywords

infrasound signal recognition convolutional neural network long short-term memory network attention mechanism deep learning

Introduction

Infrasound, defined by frequencies below 20 Hz, exhibits significant long-range propagation capabilities,^1–5 holding substantial application value in critical fields such as natural disaster early warning^6–9 and nuclear explosion monitoring.^10–13 For infrasound signal classification tasks, feature extraction is of paramount importance. Traditional feature extraction methods are primarily categorized into two types: domain transformation approaches based on signal transforms (e.g., Fourier transform, wavelet transform)^14–17 can enhance feature separability but often incur the loss of information from other domains; conversely, concatenation methods based on feature combination preserve multi-domain characteristics^18–20 but face challenges associated with the complexity of feature selection.

Deep learning methods offer novel solutions to this challenge. Convolutional Neural Networks (CNNs) are capable of automatically learning spatial features within signals,^9,21 yet exhibit limitations in temporal modeling. In contrast, Bidirectional Long Short-Term Memory networks (BiLSTMs) excel at capturing long-range temporal dependencies but are less effective at extracting spatial features. To overcome these limitations, this study proposes an Attention-CNN-BiLSTM recognition method: initially, raw infrasound signals undergo wavelet threshold denoising, and their power spectral density (PSD) is computed to serve as the input; subsequently, a CNN-BiLSTM hybrid architecture²² is constructed, where the CNN module extracts time-frequency spatial features and the BiLSTM module models temporal dependency relationships; concurrently, a channel-spatial attention mechanism, specifically the Convolutional Block Attention Module (CBAM),²³ is introduced to enhance the focus on critical features. This design effectively augments the model’s capacity to represent the complex characteristics of infrasound signals, ultimately enhancing classification accuracy.

Infrasound signal preprocessing

To address the vulnerability of infrasound signals’ low-frequency characteristics to noise interference, this study employs a preprocessing method based on wavelet threshold denoising fused with power spectral density analysis.

Wavelet threshold denoising

The wavelet threshold denoising method adopted in this study²⁴ achieves signal-noise separation by leveraging the time-frequency localization properties of wavelet transform. This approach constructs an adaptive time-frequency analysis framework based on the rapid attenuation (sign-alternating and zero-mean characteristics in the time domain) and energy concentration in the frequency domain of wavelet basis functions. Specifically, higher temporal resolution with narrow time windows captures transient abrupt features in high-frequency infrasound components, while broader time windows characterize long-period trends in low-frequency components. Subsequently, noise suppression is implemented through threshold shrinkage of wavelet coefficients.

Compared to the global basis function constraints of Fourier transform, this dual-resolution mechanism demonstrates unique advantages for processing non-stationary infrasound signals, enabling precise retention of event-critical features while effectively filtering noise. The fundamental expression of wavelet transform can be formulated as

W (a, b) = \int_{- \infty}^{\infty} x (t) ψ_{a, b} (t) d t

(1)

where

x (t)

represents the input signal,

ψ_{a, b} (t)

denotes scaled and shifted versions of the wavelet basis function,

a

is the scaling parameter, and

b

is the translation parameter. The core principle of wavelet threshold denoising lies in the discriminative processing of wavelet coefficients: it achieves signal-noise separation by suppressing noise-dominated coefficients while preserving feature-critical coefficients associated with the signal.

For threshold selection, this study employs Stein’s Unbiased Risk Estimate (SURE) threshold.²⁵ This method determines the optimal threshold by minimizing the mean squared estimation error, demonstrating superior denoising performance in non-stationary infrasound noise environments.

Power spectral density

PSD²⁶ characterizes the distribution of signal energy across the frequency domain, providing a discriminative physical criterion for separating seismic and chemical explosion infrasound signals. By suppressing phase information interference, it focuses on intrinsic energy distribution differences in characteristic frequency bands. Leveraging this principle, this study employs a PSD-based signal processing approach to construct joint energy-frequency feature representations. Compared to raw time-domain waveforms, this method effectively enhances inter-class decision boundaries and reduces sensitivity to noise. The mathematical expression of PSD is given by

S_{x} (f) \lim_{T \to \infty} \frac{1}{T} {| X_{T} (f) |}^{2}

(2)

where

X_{T} (f)

is defined as the Fourier transform of signal

x (t)

over the time window

T

. Figure 1 shows the waveform and PSD.

Figure 1.

Time-domain waveform and power spectral density spectrum of infrasound signals.

Attention-CNN-BiLSTM classification model

The low-frequency characteristics, long-term temporal patterns, and high noise susceptibility of infrasound lead to inefficient feature extraction and selection in traditional manual approaches. While deep learning models can automatically learn features, single-architecture solutions face fundamental limitations: CNNs effectively capture spectral patterns through local perception and translation invariance, yet struggle to model long-term temporal dependencies due to fixed receptive fields, with pooling operations potentially discarding critical information. LSTMs establish long-range dependencies via gating mechanisms but exhibit insensitivity to local features, alongside challenges including poor noise robustness, high computational complexity, and overfitting risks in limited-data scenarios.²⁷

CNN-LSTM hybrid models achieve mutual compensation of strengths through collaborative specialization—the CNN extracts local time-frequency features while reducing dimensionality and suppressing noise, then the LSTM analyzes contextual feature relationships. This preserves information integrity while enhancing feature representation capability. Nevertheless, the complex spatiotemporal feature of infrasound signals still exposes dual bottlenecks: unidirectional LSTMs process all timesteps indiscriminately, failing to focus on critical event segments, and the absence of dynamic feature selection mechanisms allows noise to interfere with decision boundaries.

To address these challenges, this study proposes an Attention-CNN-BiLSTM hybrid architecture: Bidirectional LSTM (BiLSTM) jointly analyzes historical and future contexts to interpret events, while embedded attention mechanisms dynamically weight critical features. This achieves synergistic optimization of noise suppression and discriminative feature enhancement, providing a more comprehensive solution for complex infrasound signal classification. The detailed model structure is illustrated in Figure 2.

Figure 2.

Schematic diagram of the attention-CNN-BiLSTM model architecture.

When using the Attention-CNN-BiLSTM model for infrasound signal classification, the input layer first receives the preprocessed PSD of the infrasound signal as input, which is then fed into the CNN module for feature extraction. The PSD clearly characterizes the energy distribution of the signal across different frequency components. The CNN module utilizes convolutional kernels sliding along the frequency axis to effectively extract localized energy concentration patterns in specific frequency bands. Subsequently, these features are passed to the CBAM attention module—which is embedded before the fully connected layer of the CNN in this study—where adaptive weighting across both channel and spatial dimensions enhances discriminative frequency bands that significantly contribute to classification while suppressing irrelevant or noise-dominated regions. The features optimized and weighted by CBAM are flattened into a sequential format via a Flatten layer and then input into the BiLSTM network for temporal modeling. BiLSTM captures long-range dependencies in the signal from both forward and backward temporal directions using bidirectionally chained LSTM units, thereby effectively compensating for the limitations of CNN in contextual modeling. Finally, the high-level temporal features output by BiLSTM are integrated through a fully connected layer and converted into probability outputs for each class via a Softmax function, accomplishing end-to-end classification. The operational principles of each module are detailed below:

(1) CNN Feature Extraction Module. The CNN is a deep learning architecture whose core innovation lies in three fundamental mechanisms: local connectivity, weight sharing, and hierarchical feature extraction. These mechanisms significantly reduce network parameters while effectively capturing spatial hierarchies in data.²⁸ A typical CNN comprises three primary feature-processing layers:

Convolutional layers employ learnable kernels to perform sliding-window operations on inputs, extracting local features and generating feature maps. Pooling layers reduce feature dimensionality through down sampling, enhancing translation invariance and robustness. Fully connected layers integrate high-level abstract features, with the final classification executed by a Softmax layer.

Through hierarchical feature transformation, CNNs achieve progressive representation learning from low-level features to high-level semantic abstractions. This characteristic endows them with exceptional performance in image recognition, seismic event detection, and signal classification tasks. Particularly when processing high-dimensional data with strong local correlations, their representational efficiency significantly outperforms traditional fully connected neural networks.

(2) CBAM Attention Module. The attention mechanism enables neural networks to dynamically focus on critical features within input data through selective information processing, enhancing model discriminative capability under noise interference. The adopted CBAM is a lightweight yet efficient feedforward convolutional attention module that achieves this via a cascaded feature refinement pathway: Its channel attention submodule employs global pooling and multilayer perceptron to generate frequency-band importance weights, adaptively enhancing infrasound event-relevant spectral bands. The spatial attention submodule learns spatial weight distributions through convolutional operations, precisely localizing signal onset points and envelope characteristics. For infrasound event classification, CBAM’s dual-dimensional cooperative mechanism demonstrates core advantages, refining input features through integrated channel and spatial attention. This design enables simultaneous focus on meaningful channels and spatial regions, thereby enhancing representational capacity and decision-making accuracy.

The module structure is illustrated in Figure 3.

Figure 3.

CBAM structure.

(3) BiLSTM Temporal Modeling Module. BiLSTM is an enhanced recurrent neural network architecture developed to address the limitation of conventional LSTMs that rely solely on unidirectional temporal information.²⁹ As illustrated in Figure 4, its core principle involves processing input sequences through two independent LSTM layers: one analyzing forward temporal dependencies and the other processing backward temporal contexts. The outputs from both directions are concatenated to form comprehensive representations, thereby achieving holistic modeling of time-series data.

Figure 4.

Workflow of the BiLSTM network.

Forward LSTM processes the input sequence chronologically (from $t = 1$ to $t = T$ ), capturing information flow from past to future. This component discerns event initiation characteristics, which are critical for learning triggering mechanisms. Backward LSTM processes the sequence inversely (from $t = T$ to $t = 1$ ), acquiring information flow from future to past. This module identifies event termination patterns, essential for understanding conclusion phases and subsequent impacts. The BiLSTM output is generated by concatenating or summing the outputs of both directional LSTMs. This process is mathematically formulated as

\vec{h_{t}} = L S T M_{f o r w a r d} (x_{t}, \vec{h_{t - 1}})

(3)

\overset{\leftarrow}{h_{t}} = L S T M_{b a c k w a r d} (x_{t}, \overset{\leftarrow}{h_{t - 1}})

(4)

h_{t} = [\vec{h_{t}}; \overset{\leftarrow}{h_{t}}]

(5)

where

\vec{h_{t}}

denotes the hidden state of the forward LSTM at timestep

t

\overset{\leftarrow}{h_{t}}

represents the hidden state of the reverse LSTM at timestep

t

, and

h_{t}

is the final output obtained through concatenation. This bidirectional information fusion enables BiLSTM to simultaneously incorporate past and future contexts, thereby comprehensively capturing temporal dependencies within sequential data and optimizing classification performance.

The proposed Attention-CNN-BiLSTM demonstrates significant advantages over traditional CNN-LSTM models. When infrasound signals undergo Fourier transform, their PSD manifests as time-frequency spectrograms, encapsulating both spectral energy distribution and temporal dynamics. Conventional CNN-LSTM architectures often exhibit critical limitations in processing such data: they ineffectively discern inter-band significance levels and remain susceptible to noise contamination in irrelevant frequency bands, consequently degrading classification accuracy. In contrast, Attention-CNN-BiLSTM integrates the CBAM attention mechanism, which autonomously learns importance weights for distinct spectral bands. Simultaneously, its BiLSTM component provides contextual modeling capabilities, effectively mitigating unidirectional LSTM limitations. This dual enhancement enables the detection of forward and reverse temporal evolution patterns within PSD’s spectral energy distributions. By leveraging bidirectional dependencies, the model extracts discriminative features through comprehensive signal context analysis.

Furthermore, to effectively control model complexity and mitigate the risk of overfitting, multiple regularization techniques are incorporated into both the model architecture and the training process. Specifically, Dropout layers are inserted into the fully connected layers to randomly discard a certain proportion of neuronal units, thereby reducing overfitting. Additionally, an L2 regularization term is introduced during the training optimization process to constrain model complexity and further prevent overfitting.

Infrasound signal classification based on attention-CNN-BiLSTM

Experimental data

The data employed in this study comprise infrasound data collected from the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) monitoring network and publicly accessible data from the Library of Typical Infrasonic Signals (LOTIS).^30,31 The CTBTO dataset contains 789 infrasound waveforms generated by chemical explosions and seismic events, compiled into an infrasound waveform catalog. Table 1 enumerates event and waveform counts for these two categories. Each waveform captures a complete event with 3000 data points sampled at 20 Hz, specifically designed to validate classification accuracy in operational scenarios. To mitigate the risk of overfitting caused by insufficient data, two augmentation strategies—additive Gaussian noise and time-shifting—are applied to the training set. Gaussian noise is injected at five intensity levels (0.01–0.05, step 0.01), while time shifting is performed with six offsets (5–30 points, step 5). Every original sample is transformed by all parameter combinations, yielding 30 unique variants. Consequently, the training set is expanded 30-fold to 16 350 samples, greatly increasing diversity and forcing the model to learn robust features instead of memorizing idiosyncratic details.

Table 1.

Quantities of infrasound events and waveforms.

	Event count	Waveform count
Chemical explosion	28	386
Earthquake	8	403

The LOTIS dataset encompasses four categories: 112 aurora-induced atmosphere gravity waves (AGW), 110 mountain associated waves (MAW), 58 microbaroms (MB), and 63 volcanic eruption (VE) signals. All signals are standardized to 768 data points. LOTIS inclusion primarily assesses the method’s generalization capability, extending category diversity to evaluate the model’s discriminative capacity across heterogeneous infrasound sources. However, since the studies in Refs. 30 and 31 did not employ data augmentation when using this dataset, to facilitate a direct comparison of results and highlight the core advantages of our model, this paper also deliberately refrains from applying data augmentation. Instead, rigor and reliability of the results are enhanced through the application of the aforementioned regularization techniques and the adoption of a more stringent evaluation protocol.

The event information corresponding to the infrasound signals from natural earthquakes and chemical explosions is shown in Tables 2 and 3, respectively.

Table 2.

Infrasound event information for natural earthquakes.

Event time	Event trigger time	Latitude	Longitude	Event
20020104	19:38:08.0	32.4	−115.1	Western US Earthquake
20020319	22:14:49.9	30.1	−113.7	Gulf of California Earthquake
20041226	01:00:00.0	5	95	Sumatra M9 Earthquake
20010904	12:45:53.0	37.1	−104.7	Colorado Earthquake
20050410-2	11:15:00.0	−1.7	99.8	Mentawai M6.5 Earthquake

Table 3.

Infrasound event information for chemical explosions.

Event time	Event trigger time	Latitude	Longitude	Event
20020920	00:38:03.0	−31.0108	136.7756	Woomera Test Explosion
20050321	20:46:22.8	41.131	−112.896	UTTR Missile Detonation
20050323	19:20:00.0	29.374	−94.939	BP Refinery Explosion
20051113	05:41:00.0	43.85	126.57	Jilin Chemical Factory Explosion
20080315	11:10:06.0	41.406	19.632	Albania Arms Depot Explosions

To ensure statistical rigor in model evaluation, this study adopts an event-isolated data partitioning strategy: all infrasound data originating from the same event are exclusively assigned to either the training or testing subset. This approach prevents cross-contamination between homologous events during training and testing phases, thereby eliminating data leakage risks arising from signal propagation path similarity and event-specific feature redundancy. This fundamental safeguard ensures classification reliability.

Evaluation metrics and parameter settings

The primary evaluation metric for the classification results in this paper is the confusion matrix, as shown in Table 4. The rows of this confusion matrix represent the actual classes, while the columns represent the predicted classes.

Table 4.

Confusion matrix illustration.

	True value
Predicted value	TP	FP
	FN	TN

The experimental evaluation of the accuracy (Acc), precision (P), recall (R), and F1 score (F1) are calculated using TP, FP, TN, and FN, defined as follows

A c c = \frac{T P + T N}{T P + F P + T N + F N},

(6)

P = \frac{T P}{T P + F P},

(7)

R = \frac{T P}{T P + F N},

(8)

F 1 = \frac{2 \times R \times P}{R + P} .

(9)

Regarding parameter settings, the Adam optimizer is used for model training, with an initial learning rate of 0.001 and L2 regularization to prevent overfitting. The maximum number of iterations is set to 300, with the learning rate decreasing to 0.1 times the initial value every 200 training cycles. The dropout rate is set to 0.5. The BiLSTM comprises 3 hidden layers, each with 256 units. This configuration references successful experiences in similar tasks from relevant literature.³¹ The number of neurons in the fully connected layer is set to match the number of event categories.

Infrasound signal classification using classical deep learning models

To validate the classification performance advantages of our proposed hybrid model, we constructed a benchmark framework encompassing two fundamental deep learning architectures—CNN and Recurrent Neural Network RNN. The framework incorporates: LeNet-5³² as a representative shallow CNN, AlexNet³³ and ResNet-18³⁴ representing classical and deep CNNs, respectively, and LSTM as the methodological exemplar for temporal modeling. All experiments utilize chemical explosion and seismic events dataset. Experimental results are presented in Table 5:

Table 5.

Classification results of four networks.

	ACC/%	P/%	R/%	F1/%
LeNet-5	72.13	56.41	79.52	66.00
AlexNet	74.18	70.09	74.55	72.25
ResNet-18	74.59	74.34	73.11	73.71
LSTM	75.82	76.92	73.77	75.31

Table 5 experimental results demonstrate distinct performance characteristics among the four neural networks in infrasound signal classification. The LeNet-5 model achieved 72.13% overall accuracy but exhibited significant recall deficiency in chemical explosion signal classification due to its shallow architecture constraints. The ResNet-18 model attained 74.59% classification accuracy through residual connections, showing exceptional training stability. Notably, the LSTM model achieved the highest performance among single architectures at 75.82% accuracy, underscoring the critical importance of temporal modeling for infrasound signal processing.

These findings lead to following principal conclusions: (1) Network depth significantly impacts the feature extraction capability of the models. The shallower LeNet-5 network yielded the lowest accuracy, whereas the deeper AlexNet and ResNet-18 architectures achieved higher classification performance; (2) The LSTM model delivered the best performance in this classification task, attaining the highest accuracy. This underscores the importance of temporal modeling in infrasound signal processing; (3) Different neural network architectures exhibit distinct advantages in feature extraction: CNN structures are more suitable for extracting spatially local features, while LSTM excels at modeling temporal sequences. Therefore, single-model approaches face limitations in representing multidimensional infrasound signal features. Exploring multi-model fusion or feature complementarity strategies—combining CNN’s spatial feature extraction capability with LSTM’s temporal modeling strength—holds promise for further enhancing classification performance.

Given that ResNet-18 achieved high accuracy, it is subsequently adopted as the backbone network for CNN feature extraction. All architectural parameters—including the kernel sizes and the number of layers—are strictly retained from the original design,³⁴ except that all two-dimensional kernels are converted to one-dimensional kernels.

Infrasound signal classification using CNN-LSTM

An infrasound signal processing framework based on a CNN-LSTM fusion model is employed for classification. This framework incorporates specialized structural adaptations that address the low-frequency characteristics and long-term temporal patterns of infrasound signals. Its architecture comprises an input layer, convolutional module, dimensionality reduction layer, LSTM layer, and output layer. The detailed structure is illustrated in Figure 5.

Figure 5.

Structural diagram of CNN-LSTM architecture.

Employing the structure illustrated in Figure 5 for infrasound signal classification, the classification results are presented in Figure 6.

Figure 6.

Classification results of CNN-LSTM network.

Figure 6 experimental results demonstrate the CNN-LSTM hybrid architecture’s significant advantages for infrasound signal classification, achieving 78.28% overall accuracy—surpassing single-model baselines by an average of 4.1%. Notably, its seismic signal recognition rate reaches 80.30%, exceeding the optimal standalone model (LSTM) by 5.5 percentage points. This enhancement primarily stems from the cascaded design enabling spatiotemporal feature synergy: the CNN front-end extracts discriminative dimensionality-reduced features for LSTM, while the LSTM back-end compensates for CNN’s limitations in global temporal modeling. This superiority proves particularly pronounced for seismic infrasound classification, as such signals simultaneously exhibit distinctive spatial patterns (captured by CNN) and characteristic temporal evolution (modeled by LSTM). These findings conclusively validate that combining complementary neural network strengths constructs high-performance classifiers tailored to infrasound’s complex characteristics.

Infrasound signal classification using attention-CNN-BiLSTM

Compared to traditional CNN-LSTM, Attention-CNN-BiLSTM leverages BiLSTM’s bidirectional temporal modeling capability, effectively mitigating the limitations of unidirectional LSTMs. Furthermore, the integration of the CBAM attention mechanism enables dynamic focus on critical features within input data, significantly enhancing the model’s discriminative capability under noise interference. The CBAM module is employed with its default parameter configuration, wherein the compression ratio of the channel-attention submodule is set to 16. This default has been validated across multiple vision tasks for both strong performance and robustness, and is therefore adopted unchanged in this work. Classification results are shown in Figure 7:

Figure 7.

Classification results of attention-CNN-BiLSTM network.

Figure 7 experimental results demonstrate the superior comprehensive performance of the Attention-CNN-BiLSTM model for infrasound signal classification, achieving 85.25% overall accuracy-significantly outperforming all previously evaluated neural architectures. Specifically, the model attains 87.18% precision and 85.00 F1 value in chemical explosion signal recognition, exceeding the CNN-LSTM baseline by nearly 7 percentage points. These findings conclusively validate that the integration of CNN’s spatial feature extraction, BiLSTM’s bidirectional temporal modeling, and attention-based feature selection constructs a classification methodology with enhanced discriminative power.

Consequently, we deployed this optimized model on the public infrasound dataset (LOTIS), with experimental outcomes presented in Figure 8:

Figure 8.

Classification results based on public infrasound dataset.

As shown in Figure 8, the Attention-CNN-BiLSTM achieves a classification accuracy of 98.06% on a public dataset with multiple infrasound event categories, proving its good generalization ability.

Discussion

In this section, a systematic evaluation of the contributions of each core module in the proposed model and its overall performance was conducted through four analytical experiments. First, an ablation study was carried out to quantify the individual contributions of the CBAM, CNN, and BiLSTM modules to the overall performance improvement. Second, to validate the practical deployment feasibility of the model, a comparative analysis of the time consumption during both training and inference stages was performed. Subsequently, statistical significance tests were employed to verify the reliability of the model’s performance superiority from a statistical perspective. Finally, to investigate the generalization capability of the method, cross-task validation was conducted using the publicly available Case Western Reserve University (CWRU) bearing fault diagnosis dataset.

Ablation study

To validate the effectiveness of individual modules in enhancing the classification performance of the Attention-CNN-BiLSTM model, this study designed an ablation study for quantitative analysis. The experiment consists of the following three comparative configurations:

BiLSTM Only: Initially, both the CNN and CBAM modules are removed, and only a standalone BiLSTM classifier is utilized. This configuration is designed to evaluate its performance as a recurrent neural network baseline, while also enabling comparative analysis with a standard LSTM to validate the superiority of BiLSTM in capturing long-range contextual dependencies.

CNN-BiLSTM: At this stage, a CNN feature extractor is incorporated into the BiLSTM network. To maintain experimental consistency, the ResNet-18 architecture is retained as the CNN module. This experiment aims to quantify the performance improvement resulting from the addition of the CNN module.

Attention-CNN-BiLSTM: Finally, the CBAM attention mechanism is integrated into the CNN-BiLSTM structure, forming the complete proposed model. This experiment is designed to isolate and quantify the additional performance gain contributed by the CBAM mechanism. The results are shown in Table 6.

Table 6.

The results of the ablation experiment.

	ACC/%	P/%	R/%	F1/%
BiLSTM	77.87	78.63	76.03	77.31
ResNet18-BiLSTM	81.15	78.63	81.42	80.00
Attention- ResNet18-BiLSTM	85.25	87.18	82.93	85.00

Table 6 presents the ablation study results, which validate the effective contribution of each module to the classification performance. Firstly, when BiLSTM replaced LSTM for classification, all evaluation metrics improved by an average of approximately 2.00%. This indicates that the BiLSTM network can more effectively capture contextual dependencies in temporal data, thereby enhancing temporal modeling capabilities. Upon introducing the ResNet-18 module on this basis, the metrics further improved by an average of 2.84%, with metric R notably increasing by 5.39%. This result demonstrates that employing ResNet-18 for spatial feature extraction prior to temporal feature modeling enhances feature representation capabilities, providing more discriminative features for subsequent classification. Finally, with the incorporation of the CBAM attention mechanism, all evaluation metrics reached their highest levels. The attention module dynamically selects and weights important features while suppressing irrelevant ones, significantly improving feature separability and further optimizing classification performance.

In summary, the ablation study systematically validates the progressive contributions of each module: BiLSTM enhances temporal modeling capabilities, ResNet-18 improves spatial feature extraction, and the CBAM attention mechanism further refines model performance through feature selection. The synergistic effect of these modules ultimately leads to a significant improvement in classification performance, fully demonstrating the effectiveness and necessity of each component in the proposed architecture.

Deployment feasibility

In terms of the training environment configuration, the experiments are conducted using MATLAB 2024a, equipped with an NVIDIA RTX 5000 Ada Generation Laptop GPU and an 13^th Gen Intel(R) Core(TM) i9-13980HX CPU. Regarding the time consumption of the experiments, the training and testing durations for each method are summarized in Table 7:

Table 7.

The results of the training and testing time.

	Training time/s	Testing time/s
LSTM	32	0.12
BiLSTM	43	0.22
ResNet-18	81	0.47
ResNet18-LSTM	103	0.58
ResNet18-BiLSTM	117	0.71
Attention- ResNet18-BiLSTM	119	0.71

As shown in Table 7, although the Attention-ResNet18-BiLSTM model requires an additional 87 s of training time compared to LSTM, its total training time is only 119 s, with a mere 0.71 s needed for testing. More importantly, the model achieves a significant accuracy improvement of 9.43%. In practical deployment scenarios, since only pre-trained model inference is required, the actual application time depends solely on the testing duration. This exceptionally short inference time demonstrates the model’s excellent deployment feasibility. Therefore, while significantly improving classification performance, this method also exhibits outstanding engineering application prospects.

Statistical significance

This section conducts significance analysis on the results of each model, where BiLSTM is denoted as Model 1, ResNet18-BiLSTM as Model 2, and Attention-ResNet18-BiLSTM as Model 3. Statistical analysis was conducted on the experimental results of each model from 10 independent trials. Initially, the 95% confidence intervals of the mean values for each model were calculated. As shown in Table 8, the performance estimation intervals of the three models do not overlap with each other, preliminarily indicating significant differences between the models. Furthermore, one-way analysis of variance (ANOVA) was employed to examine the overall differences among the models. The obtained p-value was significantly below the 0.05 significance level (p < 0.0001).

Table 8.

Significance analysis of the results for each method.

Comparison group	Mean difference	95% Confidence interval	p-value	Significance
Model 2 vs Model 1	+3.37	[2.38, 4.36]	<0.0001	Significant
Model 3 vs Model 1	+6.99	[6.00, 7.98]	<0.0001	Significant
Model 3 vs Model 2	+3.62	[2.63, 4.61]	<0.0001	Significant

The results in Table 8 demonstrate that: First, there exist essential differences in the performance among the three models, which are not caused by random factors but are statistically significant; Second, post-hoc multiple comparison analysis reveals that the performance differences between any two models all reach statistically significant levels (p < 0.05); Third, in terms of performance ranking, the proposed method (Model 3) significantly outperforms the other two comparative models, and its performance improvement is supported by reliable statistical evidence.

In summary, these results not only numerically validate the superiority of the proposed method but also confirm the significance of this performance advantage from statistical inference perspectives, thereby ensuring the reliability and scientific rigor of the experimental conclusions.

Generalization verification

To validate the generalization capability of the Attention-CNN-BiLSTM model, this study utilizes the Case Western Reserve University (CWRU) bearing fault dataset. The dataset comprises normal bearings and three types of faults: inner race fault, outer race fault, and rolling element fault. Each fault category contains three different fault diameters, resulting in a total of 10 distinct classes labeled from “0” to “9.” For experimental validation, data collected at a sampling frequency of 12 kHz under a rotational speed of 1797 r/min were employed. A sliding window approach with a length of 1024 data points and a stride of 200 was applied to the raw signals to generate samples, yielding 200 samples per category. The dataset is partitioned into training and testing sets with a 7:3 ratio. This sampling strategy ensures the creation of a multi-class dataset with perfectly balanced sample distribution across all categories, which enables more effective validation of the proposed method’s effectiveness. The results are shown in the Figure 9.

Figure 9.

Classification results based on CWRU bearing fault dataset.

As can be seen from Figure 9, the classification accuracy based on the CWRU bearing fault dataset exceeds 99%. These results strongly demonstrate the model’s exceptional generalization capability and ultimately validate the effectiveness of the proposed Attention-CNN-BiLSTM architecture.

Conclusion

Traditional infrasound signal recognition methods exhibit low efficiency and struggle to meet application demands in complex environments. Addressing this limitation, this study proposes an Attention-CNN-BiLSTM-based classification methodology aimed at discriminating chemical explosions from seismic signals. Through rigorous experimentation and analysis, the following principal conclusions are drawn:

(1) The four neural architectures exhibit distinct performance variations in infrasound classification tasks. Among standalone models, the temporal modeling capability of LSTM—leveraging its strength in capturing long-range dependencies—yields optimal performance. These results confirm that network depth and temporal modeling capability constitute two critical architectural factors governing infrasound classification efficacy.

(2) The CNN-LSTM hybrid architecture achieves 78.28% overall accuracy through spatiotemporal feature synergy, surpassing the optimal standalone model by 5.5 percentage points. This conclusively validates the hybrid design’s effectiveness for processing infrasound signals exhibiting concurrent spatial patterns and temporal evolution characteristics.

(3) The further-optimized Attention-CNN-BiLSTM model elevates accuracy to 85.25%, attaining 98.06% accuracy on the public LOTIS dataset. These outcomes validate the critical role of integrated spatiotemporal feature modeling and attention mechanisms in enhancing infrasound classification performance.

Footnotes

ORCID iD

Xi-hai Li

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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