Audio-Only Ensemble Learning

Updated 29 November 2025

The paper presents an audio-only ensemble framework that fuses diverse base models (e.g., SVMs, DNNs, Transformers) using strategies like stacking, soft-voting, and feature concatenation to enhance task performance.
The methodology emphasizes rigorous audio preprocessing and feature extraction—using MFCCs, mel-spectrograms, and other descriptors—to build high-dimensional, discriminative input representations.
Results highlight significant improvements in accuracy and robustness across applications such as emotion recognition, condition monitoring, and medical screening, with metrics like 94.2% and 97.6% accuracy reported in key studies.

Audio-only ensemble learning frameworks are machine learning architectures that leverage multiple predictive models to jointly process audio signals for downstream tasks such as classification, detection, or representation learning. These frameworks exclude visual, textual, or other modalities and instead exploit the diversity and complementarity among audio-based representations and classifiers. Recent research demonstrates that audio-only ensembles yield superior accuracy, robustness to noise, and computational efficiency, often outperforming individual models. Ensembles are applied across a variety of domains, including speech emotion recognition, multimedia scene understanding, condition monitoring, medical screening, and self-supervised representation learning (Xiong et al., 22 Nov 2025, Tao et al., 2018, Pillai et al., 14 Sep 2025, Wu et al., 2022, Nadkarni et al., 8 May 2024, Chang et al., 2022, Ristea et al., 2021, Kataria et al., 2020).

1. Architectural Principles and Ensemble Typologies

Audio-only ensemble learning systems typically operate by combining base models (e.g., SVMs, DNNs, CNNs, Transformers) that take audio-derived feature vectors as input and output class probabilities or high-dimensional embeddings. Fusion is achieved via stacking, soft-voting, score weighting, or feature concatenation. The general workflow is:

Preprocessing and feature extraction from raw audio (segmentation, spectral analysis, domain normalization)
Independent training of multiple base models, each with distinct architectures, feature views, or optimization objectives
Aggregation of outputs using ensemble strategies, such as soft-voting, stacking with meta-learners, weighted linear fusion, or feature-level concatenation
Final decision, confidence calibration, or task-oriented post-processing

For instance, a stacking architecture may use ten SVMs and six neural networks as base learners whose outputs are fused by an RBF-kernel meta-SVM for three-class emotion classification in movie scenes (Xiong et al., 22 Nov 2025). Alternatively, score-level fusion via weighted sum (based on log-likelihood ratio minimization) can integrate DNN, SVM, and RNN sub-systems for emotion recognition (Tao et al., 2018). Feature-level ensembles fuse deep SSL embeddings from multiple models for holistic representation (Wu et al., 2022).

Table: Ensemble Composition Examples

Framework / Paper	Base Models	Fusion Method
(Xiong et al., 22 Nov 2025)	10 SVMs + 6 NNs	Stacking with meta-SVM
(Pillai et al., 14 Sep 2025)	SVM, RF, XGBoost	Soft-voting (equal weights)
(Tao et al., 2018)	DNN (IS10/iVector), SVM, RNN	Linear score fusion
(Wu et al., 2022)	Wav2Vec 2.0, HuBERT, CREPE	Feature concatenation

2. Feature Extraction and Engineering

Ensemble frameworks employ rigorous audio preprocessing pipelines to maximize discriminative information. Common steps include:

Segmentation into fixed-duration clips (e.g., 7–10 s)
Extraction of spectral, time-domain, and time-frequency descriptors:
- MFCCs, mel-spectrogram energies, chroma, spectral centroid, roll-off, ZCR, temporal energy, wavelet bands
- Large functionals (e.g., IS10, i-vectors, LLDs over frames)
- Higher-order statistics: mean, range, MAD, standardized scaling
Outlier removal, normalization (Min–Max, z-score), and feature selection (variance thresholding, chi-square filter, data drift correction, correlation analysis)

Automated pipelines may expand raw features to >100 dimensions and employ sequential selection for final compact representations (e.g., ~80 features per 7 s audio segment (Xiong et al., 22 Nov 2025); 127 features for equipment monitoring (Pillai et al., 14 Sep 2025)).

3. Ensemble Training Paradigms

Systems implement various individual and collective training protocols:

Base learners trained separately—with architecture-specific losses, regularizers, or auxiliary tasks (e.g., multi-task DNNs for emotion/speaker/gender (Tao et al., 2018))
Hyperparameter optimization via grid search and cross-validation (K-fold, nested, or leave-one-out strategies)
Meta-learners receive cross-validated predictions as input features (stacking) (Xiong et al., 22 Nov 2025)
Self-paced learning cycles iteratively introduce high-confidence pseudo-labeled samples from the ensemble to augment the training set, promoting knowledge sharing among base models (Ristea et al., 2021)
Data augmentation (e.g., Gaussian noise, pitch/time shifting, bandpass filtering) is widely adopted, particularly for minority-class enrichment or variability (Nadkarni et al., 8 May 2024, Chang et al., 2022)
Cost-sensitive and focal loss functions counteract class imbalance (Chang et al., 2022)

The computational footprint is controlled via explicit design choices, such as reducing model size via weak encoders (Zhang et al., 10 Sep 2024), or by leveraging ensemble selection mechanisms to minimize inference cost.

4. Fusion Schemes and Statistical Evaluation

Fusion mechanisms are central to ensemble learning:

Stacking: Meta-learners (often SVMs) take base model outputs for final classification (Xiong et al., 22 Nov 2025)
Soft-voting: Weighted averaging of class-posteriors yields the ensemble decision, often with equal or cross-validated weights (Pillai et al., 14 Sep 2025, Nadkarni et al., 8 May 2024)
Feature concatenation: Channel-wise or embedding-wise fusion enhances representational capacity for downstream models (Wu et al., 2022, Zhang et al., 10 Sep 2024)
Score-weighted fusion: Optimized linear weights balance sub-system confidences for macro-average F1 (Tao et al., 2018)
Bagging: Base classifiers trained with random initialization/data orderings, with uniform averaging of outputs (Chang et al., 2022)
Self-paced aggregation: Pacing functions select high-confidence predictions for pseudo-labeling, gradually expanding the labeled set (Ristea et al., 2021)

Frameworks employ rigorous statistical protocols (McNemar’s test for pairwise significance, Friedman test for multi-algorithm ranking, Nemenyi post-hoc test) to confirm ensemble improvement over single models (e.g., 94.2% accuracy and significant p-values in condition monitoring (Pillai et al., 14 Sep 2025)).

5. Application Domains and Benchmark Results

Audio-only ensembles have achieved state-of-the-art accuracy and robustness across diverse tasks:

Emotion recognition in multimedia: Real-world film/TV datasets, multiclass emotion labels (MAF improvement +29.5% (Tao et al., 2018); 86% accuracy for “Good/Neutral/Bad” scenes (Xiong et al., 22 Nov 2025))
Industrial monitoring: Fault detection with 94.2% accuracy, robust against noise and variability (Pillai et al., 14 Sep 2025)
Respiratory disease screening: AFEN yields test accuracy of 97.6%, precision/recall for health/disease classes exceeding 90% (Nadkarni et al., 8 May 2024)
COVID-19 detection: Deep ensembles with uncertainty estimation reach AUC-ROC of 85.43% (Chang et al., 2022)
Audio deepfake tracing: Metric-learning/Conformer ensemble achieves in-domain accuracy of 95.6% and OOD stability (lowest Fréchet distance 6.93) (Kulkarni et al., 2 Jun 2025)
Self-supervised representation learning: Fusion ensembles outperform single SSL models on benchmarks for speech, music, and environmental sound, addressing “blind spots” in fine-grained pitch/onset tasks (Wu et al., 2022)

6. Extensions, Limitations, and Future Directions

Current frameworks are extensible to broader taxonomies and emerging methodologies:

Incorporation of attention-based architectures (e.g., Transformer ensembles) for enriched temporal modeling (Xiong et al., 22 Nov 2025)
MoWE approaches integrate mixtures of weak pre-trained encoders via gating mechanisms for efficient multi-task adaptation (Zhang et al., 10 Sep 2024)
Deep perceptual regularization with frozen feature extractors can enhance denoising and source separation (Kataria et al., 2020)

Limitations arise from the sole reliance on acoustic cues, potential conflict in background content, manual tuning requirements (e.g., loss weights, early-stopping), and variable generalization to unseen domains or fine-grained classes. Future research is exploring automatic fusion weight learning, on-device distillation of fused models, scalable pseudo-labeling, and integration with large audio–LLMs.

7. Representative Frameworks and Implementation Guidance

Practitioners can reproduce core approaches by following defined pipeline steps, hyperparameter settings, and fusion rules as specified in the literature. Key practices include:

Exhaustive preprocessing and feature engineering, including statistical and time-frequency methods
Diverse base model architectures and robust cross-validation
Rigorous fusion and stacking techniques with confidence calibration
Statistical validation of ensemble benefits
Empirical ablation and post-hoc analysis of fusion and augmentation effects

Explicit architectural and training protocols (feature formulas, stacking data generation, soft-voting equations, loss weight schedules, pacing function definitions) support reproducibility and allow extension to new datasets and tasks (Xiong et al., 22 Nov 2025, Tao et al., 2018, Pillai et al., 14 Sep 2025, Nadkarni et al., 8 May 2024, Chang et al., 2022, Kataria et al., 2020, Ristea et al., 2021, Wu et al., 2022, Zhang et al., 10 Sep 2024).