Environmental Sound Classification

• Environmental Sound Classification (ESC) is the process of mapping non-speech audio from environments to predefined labels, enabling applications in urban monitoring, bioacoustics, and smart systems.
• Key methods include robust feature representations like log-Mel spectrograms, MFCCs, and learned front-ends, combined with CNNs, CRNNs, and attention mechanisms to capture complex temporal and spectral patterns.
• Effective ESC systems employ diverse data augmentation, noise resilience, and compact model designs for efficient performance in resource-constrained and noisy real-world deployments.

Environmental Sound Classification (ESC) is the task of mapping arbitrary non-speech audio fragments from natural or artificial environments to predefined categorical labels such as “jackhammer”, “rain”, “glass breaking”, or “street music”. ESC is a cornerstone for applications in urban monitoring, bioacoustics, smart home systems, intelligent surveillance, and human-computer interaction. Unlike speech and music tasks, ESC systems must cope with highly diverse signal statistics, weak label regimes, and complex temporal and spectral event structures. Research in ESC has produced a diverse ecosystem of feature representations, neural architectures, data augmentation pipelines, and evaluation protocols that reflect its challenges across both large-scale research and resource-constrained deployments.

1. Problem Setting, Challenges, and Data Sets

ESC operates in a supervised or semi-supervised classification framework where the input is a monaural or polyphonic audio segment, and the output is a single class label per clip. Benchmark datasets include ESC-10 and ESC-50 (5 s clips, 10/50 classes, 5-fold cross-validation), UrbanSound8K (≤4 s urban sounds, 10 classes, 10-fold split), and DCASE scene classification collections (Zhang et al., 2018, Guzhov et al., 2020, Wang et al., 2019, Guzhov et al., 2021, Mohaimenuzzaman et al., 2021, Zhou et al., 2022, Nasiri et al., 2021, Gupta et al., 21 Sep 2024, Larroza et al., 14 Mar 2025, Dehaghani et al., 12 Nov 2025, Chen et al., 19 Sep 2025).

Key challenges unique to ESC include:

• Spectrotemporal complexity: Non-speech environmental sounds exhibit event patterns across a very broad range of time-frequency scales, from impulsive to stationary, with frequent temporal overlap and highly variable SNR (Zhu et al., 2018, Wang et al., 2019).
• Limited or weakly labeled data: Many datasets provide only clip-level labels, confounding event-localization and conferring sensitivity to long silent or irrelevant intervals (Wang et al., 2019, Zhang et al., 2019, Zhang et al., 2020).
• Data imbalance and scarcity: Escalated by rare or “rare event” classes within datasets; data augmentation and synthetic data are often used for mitigation (Madhu et al., 2021).
• Deployment constraints: Effective ESC models must generalize across device recording characteristics and remain efficient for IoT and neuromorphic computing scenarios (Mohaimenuzzaman et al., 2021, Dehaghani et al., 12 Nov 2025, Larroza et al., 14 Mar 2025).

2. Feature Representations and Front-Ends

Feature selection is foundational to ESC performance. The following representations are widely deployed:

• Time–frequency representations: Log-Mel and log-Gammatone spectrograms (Zhang et al., 2019, Guzhov et al., 2020, Wang et al., 2020, Wang et al., 2019, Zhang et al., 2018, Dehaghani et al., 12 Nov 2025). STFT parameters (e.g., window, hop, window type) are varied to capture event temporal features. Constant Q-transform (CQT) and chromagram features further augment discriminative power (Sharma et al., 2019). 2D representations are sometimes extended to multiple input channels (mel, delta, delta-delta) (Wang et al., 2020).
• Cepstral coefficients: MFCC and GFCC, stacked as input channels or used in concatenation (Sharma et al., 2019, Dawn et al., 24 Aug 2024).
• Learned front-ends: Replacement of fixed STFT with trainable complex frequency B-spline wavelet kernels (fbsp), enabling adaptive frequency decomposition tuned to the ESC task (Guzhov et al., 2021). The fbsp front-end significantly improves robustness to additive noise and sample-rate reduction.
• Raw waveform processing: Parallel multi-temporal convolutional branches operating directly on the 1D waveform to extract features relevant at multiple time scales (Zhu et al., 2018, Mohaimenuzzaman et al., 2021).
• Wavelet-domain features: DWT-based 2D spectrograms coupled with histogram equalization, engineering additional invariance to noise and local shifts (Esmaeilpour et al., 2019).
• Task-specific "audio crop": Preprocessing which preserves non-silent content by repetitive cyclic padding, shown to increase discriminative signal proportion and improve classification rates (Dawn et al., 24 Aug 2024).

3. Model Architectures and Attention Mechanisms

The evolution of ESC model architectures tracks advances in convolutional, recurrent, and attention-based deep learning:

3.1 Convolutional Architectures

• Deep CNNs: Variants with alternated asymmetric kernels, deep stacks, and multi-temporal input branches predominate, with superior performance from deeper, well-regularized models (Zhang et al., 2018, Zhu et al., 2018).
• Residual and cross-domain models: Visual domain backbones (ResNet, ResNeXt, EfficientNet) transferred to ESC, leveraging spectrogram “image” similarity and providing substantial gains with or without domain-specific attention modules (Guzhov et al., 2020, Guzhov et al., 2021, Zhou et al., 2022).
• Lightweight CNNs: For edge deployment, compact backbones (e.g., TC-ResNet-8, three-block CNNs) with custom pooling or pruning are competitive, supporting memory- and computation-constrained scenarios (Dehaghani et al., 12 Nov 2025, Mohaimenuzzaman et al., 2021, Chen et al., 19 Sep 2025).

3.2 Temporal Modeling

• CRNNs and bidirectional RNNs: Sequential recurrence via (bi-)GRU stacked on convolutional features models temporal context, yielding 2–4% gains over CNN alone (Zhang et al., 2019, Qiao et al., 2019).
• Sub-spectrogram segmentation + CNN/CRNN: Parallel models trained on segmented Mel bands with score-level fusion outperform wide-band single-branch baselines, especially for low-frequency-dominant classes (Qiao et al., 2019).

3.3 Attention Mechanisms

• Frame-level attention: Softmax- or sigmoid-weighted attention on temporal or spectrotemporal features allows the model to focus on salient event frames and suppress irrelevant or silent regions; best results are obtained when attention is applied atop the final RNN layer (Zhang et al., 2019, Zhang et al., 2020).
• Parallel temporal–spectral attention: Independent branches focus on key time frames and frequency bands using global-average-pooled and sigmoid-activated masks, recombined with learnable coefficients for robust hybridization (Wang et al., 2019).
• Channel-wise and multi-channel temporal attention: Channel-specific attention vectors exploit distinct temporal saliency (per frequency or learned channel), outperforming single-channel or no-attention methods (Wang et al., 2020).
• Feature pyramid attention: Hierarchical multi-scale aggregation of spatial and channel/temporal attention produces robust multi-resolution representations. FPAM-ResNet50 achieves large gains by spatially and temporally localizing semantically relevant activity across scale (Zhou et al., 2022).

4. Data Augmentation, Synthetic Data, and Training Protocols

Accurate ESC models rely on diverse and robust augmentation pipelines:

• Traditional augmentations: Time stretching, pitch shifting, dynamic range compression, additive ambient backgrounds (Madhu et al., 2021, Zhang et al., 2018, Zhang et al., 2019).
• Mixup: Linear interpolation of training examples and targets, enforcing linearity in the learned space, reducing overfitting, and tightening class clusters in intensity space (Zhang et al., 2018, Zhang et al., 2019, Qiao et al., 2019).
• GAN-based augmentation: Adversarial synthesis of full-length audio via class-conditional WGAN-GP (EnvGAN), filtering synthetic outputs using a numerical similarity threshold. Inclusion of synthetic data (alone or mixed 1:1 with real) increases accuracy by +5–15 percentage points, especially on smaller or imbalanced datasets (Madhu et al., 2021). Cycle-consistent GANs in the spectrogram domain further augment intra- and inter-class diversity (Esmaeilpour et al., 2019).
• Unsupervised and semi-supervised pretext tasks: Hierarchical ontology-guided coarse-to-fine pretraining (ECHO), where LLM-derived class groupings are used as targets for representation learning before fine-tuning on original classes, gives 1–8% accuracy gains over comparable fully supervised training (Gupta et al., 21 Sep 2024). Contrastive self-supervised objectives have also been explored but require large amounts of unlabelled data.

5. Dimensionality Reduction and Pooling Strategies

Reducing feature dimensionality and focusing computation on informative regions is crucial for compact and embedded ESC:

• Principal Component Analysis (PCA): Drastically reduces spectrogram input but results in intolerable loss in discriminative power for ESC tasks (e.g., 37.6% accuracy on ESC-50 vs. 66.8% for the corresponding CNN without reduction) (Dehaghani et al., 12 Nov 2025).
• Sparse Salient Region Pooling (SSRP): Instead of global pooling, this top-K or sliding-window pooling adaptively selects windows exhibiting maximal mean activation in the time-frequency domain, pushing top-1 accuracy to 80.7% (on ESC-50) at modest parameter cost (Dehaghani et al., 12 Nov 2025).
• Task-aware pooling operators: SSRP and variants serve as an embedded attention-like mechanism that can be implemented with negligible overhead relative to the main convolutional computation.

Pooling / Reduction	ESC-50 Accuracy (%)	Model/Details
CNN+Global Pool	66.8	Standard 3-block CNN
PCA+CNN	37.6	PCA (99.4% compression)
CNN+SSRP-B (W=4)	72.9	Basic salient window (one) pooling
CNN+SSRP-T (K=12)	80.7	Top-12 salient window mean pooling

6. Environment Sound Classification for Edge and Neuromorphic Computing

The need for ESC on edge devices and neuromorphic hardware drives model compression and energy-efficient representations:

• Compression and quantization: Network pruning (unstructured and structured), channel selection, and 8-bit quantization reduce model size and FLOPs by >97% with <4 percentage point drop in accuracy on ESC-50 (final model: 0.5 MB, 14.8 M FLOPs, deployable at 75 ms per inference on ARM Cortex-M4F without GPU) (Mohaimenuzzaman et al., 2021).
• Exemplar-free continual learning: AFT (Acoustic Feature Transformation) explicitly aligns old and new class features in sequential learning scenarios, employing feature knowledge distillation, cross-task feature alignment, and selective compression of prototypes. This approach outperforms previous exemplar-free methods by 3.7–3.9% and mitigates forgetting in class-incremental learning (Chen et al., 19 Sep 2025).
• Spiking neural networks (SNNs): For neuromorphic platforms, Threshold-Adaptive Encoding (TAE) yields the best trade-off for converting Mel-spectrograms to spike trains: highest F1-score (0.661), lowest spike rates, and competitive accuracy, supporting energy-efficient embedded ESC (Larroza et al., 14 Mar 2025).

7. Evaluation Protocols, Robustness, and Generalization

ESC evaluation emphasizes reproducibility, cross-dataset generalization, and robustness, with several best practices established:

• Official data splits and metrics: All major benchmarks are evaluated on official test folds; reporting of overall and per-class accuracy is standard (Guzhov et al., 2020, Wang et al., 2019, Wang et al., 2020).
• Noise and frequency robustness: Models employing adaptive front-ends (e.g., fbsp) degrade more gracefully under additive white Gaussian noise and low-pass filtering. Feature pyramid and attention designs similarly improve noise resilience (Guzhov et al., 2021, Zhou et al., 2022).
• Ablations: Advances are routinely validated via removal/addition of attention, augmentation, segmentation, and pooling modules, quantifying contributions to final accuracy (Zhang et al., 2020, Dehaghani et al., 12 Nov 2025, Wang et al., 2019, Zhou et al., 2022).
• Comparison to human and prior baselines: Recent state-of-the-art models achieve or surpass human-level accuracy on both ESC-10 (95.7%) and ESC-50 (81.3%) (Sharma et al., 2019, Guzhov et al., 2021, Guzhov et al., 2020).

ESC continues to serve as a critical domain for the development and benchmarking of pattern recognition models, offering canonical challenges at the intersection of signal processing, deep learning, and embedded AI.