Audio-Based Deep Learning Models

• Audio-based deep learning models are advanced neural architectures that process raw and transformed audio signals (e.g., waveforms, spectrograms) for tasks such as classification, segmentation, and synthesis.
• They employ diverse methodologies including CNNs, RNNs, hybrid models, transformers, autoencoders, and knowledge distillation, often integrated into end-to-end pipelines with specialized preprocessing.
• These models are applied in speech transcription, music analysis, environmental sound recognition, and deepfake detection, validated by metrics like EER, PCC, and F1 scores.

Audio-based deep learning models comprise a spectrum of neural network architectures designed to operate on raw or transformed audio signals, delivering solutions for audio classification, segmentation, synthesis, enhancement, and cross-modal retrieval. These systems leverage signal representations spanning from waveforms to time–frequency transforms and have enabled state-of-the-art results in speech, music, environmental sound processing, forensics, and quality assessment. This article systematically examines foundational representations, core model topologies, end-to-end pipelines, domain-specific applications, evaluation protocols, and current research challenges.

1. Audio Representations: From Waveform to Learned Embeddings

Audio-based deep learning architectures depend critically on the representation of the input signal. The main paradigms are:

• Raw waveform (PCM): Direct time-domain samples $x[n]$ are used in end-to-end models (e.g., WaveNet), offering maximal information but imposing heavy sequence length and modeling burdens (Natsiou et al., 2022, Božić et al., 31 May 2024).
• Short-Time Fourier Transform (STFT) and Spectrograms: The STFT,

$$X(m,k) = \sum_{n=0}^{N-1} x[n] w[n - mH] e^{-j 2\pi k n/N}$$

decomposes $x[n]$ into localized frequency bands; resulting magnitude |X(m,k)| or power $|X(m,k)|^2$ yields spectrogram representations widely used for CNN or RNN models (Purwins et al., 2019).

• Mel-spectrograms and Perceptual Filterbanks: Energy is projected onto perceptually scaled filters, such as the mel or gammatone banks,

$\mathrm{mel}(f) = 2595 \log_{10}(1 + f/700)$

resulting in log-mel (or log-gammatone) spectrograms (Natsiou et al., 2022, Božić et al., 31 May 2024).

• Constant-Q Transform (CQT): Frequency bins are logarithmically spaced, facilitating pitch-invariant analysis in MIR/music (Purwins et al., 2019).
• Learned representations and embeddings: Pretrained CNNs and transformers extract task-agnostic embeddings (e.g., PANNs, MobileNetV3, MERT), facilitating transfer to downstream tasks (Schmid et al., 2023, Jiang et al., 14 Oct 2025).

The choice and preprocessing of representation directly influences model architecture, performance, and domain transferability.

2. Core Model Architectures: CNNs, RNNs, and Advanced Hybrids

Deep learning for audio leverages a hierarchy of architectures, each targeting distinct signal properties:

• Convolutional Neural Networks (CNNs): Predominantly used with 2D spectrograms, CNNs efficiently capture local time–frequency patterns. Standard image backbones (e.g., ResNet, DenseNet, Inception) can be repurposed with minor adaptation for spectrogram input, with transfer learning from ImageNet driving strong performance (Palanisamy et al., 2020). Shallow or mobile variants (MobileNetV3) facilitate edge deployment (Schmid et al., 2023).
• Recurrent Neural Networks (RNNs): LSTM and GRU cells enable modeling of long-range temporal dependencies (Freitag et al., 2017, Yu et al., 2018). Stacked or bidirectional RNNs, as in auDeep’s seq2seq autoencoder, extract global sequence context and yield competitive unsupervised representations (Freitag et al., 2017).
• Hybrid CNN–RNN (CRNN) Models: Combined architectures exploit CNNs’ spatial feature extraction and RNNs’ sequence modeling, e.g., for music transcription and environmental audio tagging (Xu et al., 2016, Purwins et al., 2019).
• Transformer Models: Recent foundation models (e.g., MERT as in DeePAQ (Jiang et al., 14 Oct 2025), wav2vec 2.0 for singing transcription (Gu et al., 2023)) leverage deep self-attention to encode long-range dependencies and benefit from large-scale self-supervised pretraining.
• Autoencoder and Metric Learning Models: Autoencoders (including denoising and VAE variants) are used for unsupervised feature extraction (Freitag et al., 2017, Xu et al., 2016), while architectures such as DeePAQ use a ranking loss on embedding distances to learn perceptual audio quality metrics (Jiang et al., 14 Oct 2025).
• Knowledge-Distilled Light Models: Width-scaled MobileNetV3 distilled from Transformer teacher ensembles produce general-purpose embeddings transferable across audio domains at low compute (Schmid et al., 2023).

These architectures are modularly deployed: for classification, embedding extraction, sequence modeling, or end-to-end synthesis and enhancement.

3. End-to-End Pipelines and Training Regimes

State-of-the-art pipelines for audio-based deep learning often proceed through systematic stages:

• Preprocessing: Audio signals undergo normalization, resampling, duration standardization (e.g., zero-padding/truncation to fixed window), and transformation into chosen representations (STFT, mel-spectrogram, CQT, etc.) (Ngo et al., 2022, Pham et al., 1 Jul 2024).
• Front-End Feature Encoding: Feature extractors range from shallow CNNs to large pretrained models (TRILL, PANNs, wav2vec 2.0, Whisper, etc.), feeding into either direct classification heads or downstream discriminative models (Ngo et al., 2022, Pham et al., 1 Jul 2024).
• Dimensionality Reduction and Fusion: Supervised dimension-pruning can provide compact, discriminative embeddings; multi-modal or multi-view representations are fused through concatenation or late fusion, enhancing robustness (Ngo et al., 2022, Pérez et al., 2019).
• Classification and Regression: Downstream MLPs, SVMs, LightGBM, or ensemble models are used for final predictions, with fusion of heterogeneous classifiers (CNN, transformer, vision backbones, audio model embeddings) providing error reduction (Pham et al., 1 Jul 2024).
• Unsupervised and Self-Supervised Learning: Models such as auDeep employ purely unsupervised reconstruction losses, while self-supervised models (wav2vec 2.0, MERT) pretrain on large unlabeled corpora with contrastive or diversity-based objectives (Gu et al., 2023, Jiang et al., 14 Oct 2025).
• Metric Learning and Distillation: Rank-based contrastive losses, as in DeePAQ, align embedding distances to audio quality, while teacher-student knowledge distillation facilitates low-cost inference and out-of-domain generalization (Jiang et al., 14 Oct 2025, Schmid et al., 2023, Pérez et al., 2019).

Optimization employs Adam-based solvers with scheduling and early stopping, and, when appropriate, low-rank or LoRA adaptation to safely finetune large foundation models (Jiang et al., 14 Oct 2025).

4. Domain-Specific Applications and State-of-the-Art Benchmarks

Audio-based deep learning models are empirically validated across diverse domains. Examples include:

• Environmental and Scene Classification: Contextually smoothed denoising autoencoder features and “shrinking DNN” classifiers deliver low EER on DCASE (Xu et al., 2016); auDeep’s seq2seq features rival or beat MFCC and CNN baselines on ESC-10/50 and GTZAN (Freitag et al., 2017).
• Health and Forensics: Fused pre-trained embeddings (TRILL, PANN, OpenL3) with LightGBM back-ends outperform DiCOVA COVID-19 detection baselines ((Ngo et al., 2022): AUC = 89.03%, F1 = 64.41%); wavelet-noise MLP classifiers achieve >93% audio device identification (Qi et al., 2016).
• Speech and Music Transcription: Wav2vec 2.0-based singing voice transcription surpasses previous systems in both clean and noisy scenarios across multiple benchmarks, requiring orders-of-magnitude less labeled data (Gu et al., 2023).
• Audio Quality Assessment: DeePAQ’s metric-learning atop the MERT foundation model attains PCC = 0.918 and SRCC = 0.889, outperforming PEAQ and ViSQOL in both coding and unseen distortion settings (Jiang et al., 14 Oct 2025).
• Deepfake Detection: Best-practice ensembles of CNNs, vision backbones, and large audio embedding models reduce EER to 0.03 (top-3 ASVspoof 2019) via multi-representational spectrogram fusion (Pham et al., 1 Jul 2024).
• Cross-Modal Retrieval: CLIP-style architectures with pretrained PANNs and RoBERTa, trained via NT-Xent losses and data-augmented with noisy Freesound tags, yield SOTA recall on audio–text retrieval (Weck et al., 2022).

General-purpose audio embeddings, distilled from high-capacity teachers, now offer both near SOTA performance and sub-megabyte model footprints suitable for edge devices (Schmid et al., 2023).

5. Evaluation Metrics, Analysis, and Benchmarking

Multiple task- and modality-specific metrics are used to evaluate audio-based deep learning systems:

Metric	Task/Context	Key Value(s)
Classification accuracy/F1/AUC	ESC-10/50, UrbanSound8K, DiCOVA, DCASE	up to 93%+ (ESC-50) (Palanisamy et al., 2020)
Equal Error Rate (EER)	ASVspoof, environmental tagging	EER = 0.03–0.126 (Pham et al., 1 Jul 2024Xu et al., 2016)
Pearson/Spearman correlation	Audio quality/MOS prediction	PCC = 0.918, SRCC = 0.889 (Jiang et al., 14 Oct 2025)
F₁ (onset, note, offset)	SVT, MIR	COn ≥93.6% (N20EMv2) (Gu et al., 2023)

Additional metrics include mean opinion score (MOS), Fréchet Audio Distance (FAD), Inception Score, and log-likelihood—especially for generative models (Božić et al., 31 May 2024, Natsiou et al., 2022, Huzaifah et al., 2020). Patch-wise aggregation, ensembling, dimensionality pruning, and input diversity (multi-spectrum) are recognized best practices for robust metric performance (Pham et al., 1 Jul 2024).

6. Robustness, Generalization, and Efficiency

Modern audio-based deep learning research increasingly prioritizes:

• Noise-Robust Learning: Audio–visual fusion, distillation from multi-modal teachers (e.g., acoustic images and video), and background noise-aware inputs improve cross-domain generalization (Pérez et al., 2019, Gu et al., 2023, Xu et al., 2016).
• Model Compression and Edge Deployment: Structured lottery pruning with mutual information selection allows >20$\times$ model compression for generative audio models without substantial loss in quality (Esling et al., 2020, Schmid et al., 2023). Quantization (float16, INT8), global channel selection, and width scaling enable real-time inference on Raspberry Pi and microcontrollers.
• Self- and Weak Supervision: Large foundation models are efficiently fine-tuned with minimal additional parameters (e.g., LoRA for DeePAQ (Jiang et al., 14 Oct 2025)), while surrogate and self-supervised losses promote label efficiency and strong out-of-domain performance (Jiang et al., 14 Oct 2025, Gu et al., 2023).
• Transfer and Modality Fusion: Vision-model transfer on spectrogram "images" and multi-modal distillation yield SOTA in deepfake detection and environmental recognition, especially when pure audio features are unreliable (Pham et al., 1 Jul 2024, Pérez et al., 2019).

Limitations include performance drops at extreme distortion (low bitrate in DeePAQ), incomplete generalization for non-intrusive audio quality metrics, and the need for improved phase modeling and end-to-end loss alignment with perceptual criteria (Jiang et al., 14 Oct 2025, Natsiou et al., 2022, Božić et al., 31 May 2024).

7. Research Directions and Open Challenges

Current frontiers in audio-based deep learning encompass:

• Unified Audio Foundation Models: Large pretrained models integrating text, music, and audio (MERT, wav2vec, AudioLM, UniAudio) are driving zero-shot and low-shot adaptation across diverse audio domains (Jiang et al., 14 Oct 2025, Božić et al., 31 May 2024).
• Efficient Long-Context and Real-Time Processing: Sparse transformers, structured pruning, and knowledge distillation balance inference latency against fidelity and context (Schmid et al., 2023, Esling et al., 2020).
• Multi-modal and Privileged Learning: Integration of spatial, visual, and textual modalities (e.g., using acoustic images, videos, cross-modal adapters) for robust representation learning in the presence of noise and limited labels (Pérez et al., 2019, Weck et al., 2022).
• Controllable and Disentangled Synthesis: Conditioning on F0, speaker/style embeddings, and fine-tuned latent priors enables interpretable manipulation; VAEs/GANs/diffusion models are being further refined for higher disentanglement and user controllability (Natsiou et al., 2022, Božić et al., 31 May 2024).
• Objective Evaluation and Human Alignment: Lack of widely accepted, perceptually faithful automated metrics—especially for generative tasks—remains a bottleneck, motivating continued development of neural and hybrid evaluation criteria (FAD, MOS, etc.) (Jiang et al., 14 Oct 2025, Božić et al., 31 May 2024).
• Learning with Minimal Supervision: Further exploitation of weak, noisy, or cross-modal labels, self-supervised objectives, and curriculum/meta-learning paradigms targets data/hardware efficiency and out-of-distribution robustness (Jiang et al., 14 Oct 2025, Weck et al., 2022, Gu et al., 2023).

These directions emphasize the centrality of modular, robust, and computationally efficient architectures for the next wave of high-quality, scalable, and generalizable audio-based deep learning models.