Spectrogram-Based Image Classification

• Spectrogram-based image classification is a method that converts 1D signals into 2D time-frequency representations using transformations like STFT, enabling image-style analysis.
• It combines classical feature extraction techniques with handcrafted and texture-based methods to capture robust, domain-specific signal characteristics.
• Recent advances integrate CNNs, transformers, and attention mechanisms to boost performance in applications ranging from environmental sound recognition to biomedical diagnostics.

Spectrogram-based image classification is a methodological paradigm wherein 2D time-frequency representations of audio signals—the spectrograms—are treated as images amenable to a wide range of image processing, feature extraction, and deep learning techniques. This approach encompasses workflows ranging from handcrafted feature extraction and classical machine vision to modern end-to-end convolutional and transformer-based architectures, and is used across diverse domains, including environmental sound recognition, biomedical diagnostics, radar micro-Doppler analysis, and general multimodal signal classification.

1. Spectrogram Generation and Preprocessing

The starting point for spectrogram-based classification is the conversion of 1D time-series data (e.g., audio, radar, vibration) into a 2D time-frequency representation. The most common transformation is the Short-Time Fourier Transform (STFT):

$$F[l,k] = \sum_{n=0}^{N-1} f[n] \cdot w[n - l\cdot u] \cdot \exp(-j2\pi k n / K)$$

where $f[n]$ is the input signal, $w[\cdot]$ is a window of length $K$, and $u$ is the hop size, with $l$ and $k$ denoting time and frequency indices, respectively (0809.4501, Dixit et al., 2024, Sridhar, 2024).

The resulting magnitude (often log-compressed) forms the spectrogram:

$S[l,k] = \log(|F[l,k]|)$

Spectrograms may be further processed using various axis scalings (e.g., Mel-scale, constant-Q, log-frequency), color-mapping (to emphasize details when using vision models), and normalization across frequency bands or globally to produce contrast-invariant images (Wolf-Monheim, 2024, Dixit et al., 2024). For perceptually aligned representations, Mel filtering is common, mapping frequency bins to a nonlinear Mel scale better matched to human auditory sensitivity (Wolf-Monheim, 2024).

Image preprocessing may also include resizing, cropping, denoising, and data augmentation (geometric transforms, amplitude scaling, masking) to increase dataset size and network robustness (Liaquat et al., 2024).

2. Feature Extraction: Classical and Handcrafted Methods

Early spectrogram classification systems rely on feature extraction techniques motivated by both image texture analysis and audio-specific signal processing.

• Block-based texture features: By randomly sampling and matching time-frequency “blocks” from spectrograms, one can define translation-invariant texture features. For each block $B_m$, the minimum mean squared error between $B_m$ and any patch in $S[l,k]$ defines the feature $C[m]$. The resulting feature vector can be classified via $k$-nearest-neighbor or similar distance rules. Compared to classic features (MFCCs, zero-crossing rate), block-matching approaches have shown superior accuracy on instrument recognition tasks (0809.4501).
• Log-Gabor filter banks: Treating spectrograms as 2D images allows the application of multiscale, multi-orientation log-Gabor filters, resulting in a set of filtered images encoding spectro-temporal structure. Mutual information criteria select the most class-discriminative features, which are then classified using a kernel SVM. Bank-averaged log-Gabor filters have achieved up to 89.6% accuracy on environmental sound datasets (Souli et al., 2012).
• Energy projection and geometry-based feature sets: For pulse-train or tonal event detection, adaptive binarization of the spectrogram followed by projection or curve extraction (e.g., energy projections, Frangi vesselness filters, Hough transforms, active-contour refinement) yields geometric-and-intensity feature vectors that can be used with tree-based classifiers for robust event identification (Popescu et al., 2013, Serra et al., 2019).

These methods are computationally efficient and often robust in low-data or low-SNR settings, and the modularity enables easy extensions to other domains, such as seismic, radar, or bioacoustic analysis.

3. Deep Neural Approaches: CNNs and Transformer Models

Recent work uniformly treats the spectrogram as a conventional 2D image input to deep convolutional or transformer-based architectures.

• Convolutional Neural Networks (CNNs): CNNs ingest spectrograms as grayscale or (optionally) color images. Typical architectures involve progressive convolutional blocks with batch normalization, pooling, and dropout, culminating in a dense softmax layer. A canonical design uses $3\times3$ convolutional kernels, increasing channel width at each pooling stage; performance is validated on environmental sound datasets (ESC-50: Mel-spectrograms yield 57.5% validation accuracy, outperforming STFT chromagrams and CQT) (Wolf-Monheim, 2024).
• Fusion frameworks: Complementary spectrogram representations (e.g., STFT, Mel, CQT, MFCCs) may be fused at various stages (early, mid, late), or at the decision level via voting. Multi-representation late fusion achieves improved acoustic scene classification accuracy (81.8% on DCASE2020 Task1A) relative to the official baselines (Wang et al., 2020). Multi-spectrogram deep feature extraction and SVM fusion further improve robustness and accuracy, especially when combined with label expansion (multitask super-class identification) (Zheng et al., 2018).
• Transformers and Multi-View Attention: Multi-View Spectrogram Transformers (MVST) explicitly partition the spectrogram into patches of various aspect ratios, embedding each as a ViT token and fusing via gated mechanisms. This approach captures the physics of time-frequency asymmetry, achieving a specificity of 81.99% and AS=66.55% on ICBHI respiratory sound tasks—exceeding single-view CNN or naive ViT baselines by 4–8% (He et al., 2023). Temporal transformers (e.g., T-MDS-ViT) process sequences of micro-Doppler spectrograms, employing cross-axis attention and mobility-aware masking to outperform ResNet and VGG in radar target classification (94.3% accuracy vs. 85.1% for ResNet50) (Nguyen et al., 14 Nov 2025).
• Attention-guided models: CNN+Multi-head Attention superstructures learn temporal “signatures” from spectrogram sequences, improving classical tasks such as genre recognition (GTZAN, 87.3% accuracy vs. 83% for CNNs alone) and yielding interpretable evidence for class-defining moments (Sridhar, 2024).
• End-to-end learnable spectrograms: SpectNet style frontends integrate a learnable gammatone filterbank (initialized as mel-scale) as the first CNN layer. Gradients flow through both the spectrogram extraction and the classifier, yielding task-optimized, dataset-specific spectrogram representations and consistent improvements over hand-crafted filterbanks across heart sound anomaly and acoustic scene classification (Ansari et al., 2022).
• Robustness via Neural SDEs: Injecting Brownian noise at each residual block of a CNN backbone (ResNet/ConvNeXt) via Neural Stochastic Differential Equations (Neural SDEs) increases adversarial and random noise robustness, with modest drops in clean accuracy but substantial improvements in performance under strong perturbation—a critical requirement for critical infrastructure and anomaly detection (Brogan et al., 2024).

4. Latest Advances: Vision-Language and Multimodal Classification

Vision-LLMs (VLMs) such as GPT-4o can now classify spectrogram images in few-shot regimes by leveraging text-image prompting. By constructing prompts that provide cross-class spectrogram exemplars (e.g., via K-means selection on Mel embeddings), these models achieve 59% few-shot cross-validated accuracy on the environmental sound classification ESC-10 task—outperforming commercial audio-only LMs (Gemini-1.5, 49.62%) and even human visual inspection (73.75% for GPT-4o vs. 72.5% for humans, fold 1). Prompt structure, image representation, and selection of “prototypical” examples critically affect accuracy (Dixit et al., 2024).

These findings suggest three directions: (1) VLMs can advance the state-of-the-art in multimodal audio captioning via access to both spectrogram and text understanding, (2) spectrogram-based visual classification constitutes a new VLM challenge benchmark, and (3) future hybrid LMs may natively fuse both spectrogram and raw-audio representations.

5. Domain-Specific Extensions and Applications

Spectrogram-based image classification underpins advances in an array of domains:

• Bioacoustics and marine biology: Ridges (Frangi filters), Hough-based detection, and active-contour refinement identify dolphin whistles and minke whale pulse trains with high specificity and low false-positive rates (F$_1\approx$0.98, (Popescu et al., 2013); accuracy 0.977, (Serra et al., 2019)).
• Radar and remote sensing: Micro-Doppler spectrograms classify metallic objects (brass, copper, aluminum) using standard CNN algorithms, achieving >95% test accuracy on SDR-acquired data, with domain-specific data augmentation critical for small-sample regimes (Liaquat et al., 2024).
• Smart grid, anomaly detection: Spectrogram-based Neural SDE classifiers yield enhanced robustness to noise and adversarial attacks required in critical infrastructure monitoring (Brogan et al., 2024).
• Music information retrieval and audio tagging: Attention-guided models, multitask learning via label expansions, and multi-spectrogram fusion architectures are critical for robust scene and genre categorization (Zheng et al., 2018, Sridhar, 2024).

6. Performance, Robustness, and Comparative Analysis

Performance benchmarks consistently indicate that Mel-scaled spectrograms and MFCCs yield the highest validation accuracy among spectral/rhythmic features for deep CNNs (Wolf-Monheim, 2024). Multi-representation fusions, attention-based temporal pooling, and end-to-end learnable spectrograms further increase both nominal and robust accuracy across datasets such as ESC-50, DCASE, LITIS-Rouen, and proprietary radar testbeds.

Comparisons to classical acoustic features reveal the clear superiority of texture-based and deep image-based approaches (e.g., block-texture features at 85%, MFCCs/spectral moments at 70–80% (0809.4501); late fusion and label expansion CNNs at 0.9744 LITIS, 0.8865 DCASE vs. 0.8336–0.880 single-spectrogram baselines (Zheng et al., 2018)). Novel SDE-based models sacrifice a small amount of clean-accuracy for strong robustness (e.g., at $\sigma=0.25$ noise, Neural SDE yields 40.62% accuracy vs. 25.00% for the ConvNeXt baseline; at adversarial $l_2=0.05$, SDE: 21.88% accuracy vs. 0% for baseline (Brogan et al., 2024)).

7. Generalization and Future Directions

The common paradigm of spectrogram-based image classification—transformation, enhancement, (optionally) handcrafted feature extraction, and deep learning—generalizes across biomedical, environmental, industrial, and linguistic domains. Key practical recommendations include careful tuning of STFT parameters to the temporal-spectral scale of the target, judicious selection or learning of spectral filterbanks (mel/gammatone/adaptive), and the use of data augmentation/proper regularization to prevent overfitting and improve generalization.

Future directions entail:

• Full integration of learnable frontends, such as SpectNet, to optimize spectrogram features jointly with classifier weights (Ansari et al., 2022).
• Broader use of transformer models, multi-view or temporal attention, and domain-specific position encodings (He et al., 2023, Nguyen et al., 14 Nov 2025, Sridhar, 2024).
• Extension to multi-label, object-detection, and hierarchical tasks beyond single-class assignment (Harp et al., 2019).
• Hybrid multimodal models incorporating both spectrograms and raw audio in large-scale foundation models (Dixit et al., 2024).
• Robustness via SDEs or adversarially-robust architectural modifications (Brogan et al., 2024).

By leveraging spectrogram representations as images, the field continues to unify signal processing, machine vision, and deep learning, driving state-of-the-art classification performance in increasingly challenging and diverse real-world settings.