SirenPose: Dual Pipelines in Audio & 3D Vision

• SirenPose is a dual-framework concept encompassing a real-time acoustic event detection pipeline and a geometry-aware 3D scene reconstruction approach, each leveraging state-of-the-art deep learning.
• In the acoustic detection pipeline, a U-Net based model processes stereo gammatonegrams to denoise, classify (siren, horn, other), and localize alert sounds even at extremely low SNRs.
• The 3D reconstruction pipeline utilizes periodic SIREN activations and a geometry-aware loss with GNN structural priors to achieve temporally coherent and accurate dynamic scene modeling.

SirenPose refers to two technically distinct yet homonymous pipelines in the literature: (1) a real-time alert-sound detection, classification, and localization framework in noisy urban audio (Marchegiani et al., 2018), and (2) a geometry-aware loss formulation for temporally consistent dynamic 3D scene reconstruction via geometric supervision and periodic activation neural networks (Cai et al., 23 Dec 2025). Each instantiation is grounded in state-of-the-art deep learning methodologies targeted at either signal processing for automotive safety or computer vision for dynamic reconstruction.

1. SirenPose for Acoustic Event Detection and Localization

Problem Setting and Motivation

The SirenPose pipeline addresses real-time detection, classification, and horizontal localization of alerting sounds—specifically emergency vehicle sirens (yelp, wail, hi-low) and car horns—within highly non-stationary, noisy urban audio environments. The operational target is to robustly determine, from stereo microphone signals, whether a frame contains a siren, horn, or neither; to isolate these signals from unpredictable traffic noise; and to estimate the source’s azimuthal direction-of-arrival (DoA) for automotive or robotic applications (Marchegiani et al., 2018).

Traditional approaches (e.g., spectral-subtraction, adaptive filtering) struggle with the unstructured, multi-source noise typical of urban environments. SirenPose instead casts the stereo short-time spectrogram, specifically the gammatonegram, as a 2D image, enabling convolutional architectures to leverage both time and frequency context for robust denoising and event separation, even down to signal-to-noise ratios (SNR) as low as −40 dB.

2. Pipeline Architecture and Training

Data Representation and Augmentation

• Preprocessing: Input is sampled at 44.1kHz, 16-bit; analysis is performed in 0.5 s frames with a 10 ms hop size and Hamming window. Each channel is passed through a 64-band fourth-order gammatone filterbank (ERB scale, 50 Hz–22.05 kHz), yielding a per-channel gammatonegram $G\in\mathbb{R}^{64\times T}$, $T\approx 50$ per frame.
• Augmentation: Four hours of stereo urban traffic audio are recorded in Oxford. Clean siren/horn events (from UrbanSound, freesound.org) are mixed at SNRs $\in [-40\,\text{dB},10\,\text{dB}]$, with synthetic variation in DoA ($\alpha\in[-90^\circ,90^\circ]$), Doppler, echoes, and microphone characteristics, yielding 30,000 balanced 0.5 s samples.

U-Net-based Semantic Segmentation

• Input: Two-channel gammatonegrams $X \in \mathbb{R}^{64\times T\times 2}$ treated as an image.
• Architecture: A 3-layer encoder-decoder U-Net with ELU activations.
  • Encoder: Convolutional layers with increasing channel depth, interleaved with $2\times2$ max-pooling.
  • Decoder: Up-convolution, skip connections, and final $1\times1$ convolution yielding per-pixel (per time-frequency bin) softmax over {foreground, background}.
• Loss: Binary cross-entropy segmentation loss

$$\mathcal{L}_{\text{seg}} = - \sum_{f,t} \left[ y_{f,t}\log p_{f,t} + (1-y_{f,t})\log(1-p_{f,t}) \right]$$

• Denoising: Thresholded (rounded) segmentation mask is applied elementwise to the noisy input to extract the denoised alarm signal from the background.

Multi-task Classification and DoA Regression

• Classification: U-Net encoder’s latent code is passed to two fully-connected layers (256→128→3) with softmax, trained via categorical cross-entropy loss to output probabilities over {siren, horn, other}.
• Localization (DoA Regression): Denoised gammatonegrams from both channels are cross-correlated across relative delays to form a 2D input “map” to a shallow CNN (2× [conv 6×6, ELU, 2×2 pooling], FC-256, ELU, FC-1) regressed directly to azimuthal angle.
• Total Loss: Multi-task joint loss $$\mathcal{L}_{\text{SeC}} = \mathcal{L}_{\text{seg}} + \mathcal{L}_{\text{cls}}$$ optimized via Adam; localization is trained in a subsequent phase with frozen segmentation/classification networks.

3. Experimental Evaluation and Robustness

• Dataset: 30,000 labeled stereo samples, SNR $\in [-40,10]$ dB, balanced across classes.
• Performance:
  • Classification: $94\%$ overall accuracy; per-class: siren 98%, horn 90%, other 94%; accuracy resilient down to $-$30 dB SNR.
  • Localization: Median absolute error 7.5° (all classes, 0.5 s); 2.5° when aggregating over 2.5 s.
  • Ablations: Removing stereo input, segmentation, or using monaural, single-output architectures degrades localization to 11–17°.
  • Noise Robustness: Segmentation still extracts >80% of time-frequency ridges for sirens at SNR = $-$35 dB; classification ≈90%, DoA error ≈8–10°.
• Deployment Considerations: Real-time throughput at 2 Hz, total latency ≈0.6 s, model size ≈3MB (FP16), INT8 quantization retains >90% accuracy. Suitable for automotive embedded GPUs.

4. SirenPose for Dynamic 3D Scene Reconstruction via Periodic Geometric Supervision

Overview and Motivation

A second, unrelated use of the SirenPose name refers to a geometry-aware loss for reconstructing temporally coherent 3D dynamic scenes from monocular videos (Cai et al., 23 Dec 2025). Existing NeRF-like and 4D Gaussian Splatting approaches struggle with motion fidelity, multi-object interaction, occlusion, and temporal coherence. SirenPose explicitly supervises predicted 3D keypoints using a loss that leverages sinusoidal activations’ high-frequency modeling capability and structural priors expressed through keypoint graph neural networks (GNNs).

5. Pipeline Structure and Loss Formulation

Dual-Stream Architecture

• Input: Monocular video sequences.
• Low-frequency (LF) stream: Standard deformation/motion basis (e.g., CAPE) with a GNN over a keypoint graph, capturing coarse geometry and slow dynamics.
• High-frequency (HF) stream: SIREN-based implicit neural representation modeling joint spatiotemporal coordinates $(x, t)$, optimized for rapid, fine geometric changes by stacking sine-activated layers:

$$h^l = \sin\bigl(\omega_0 (W^l h^{l-1} + b^l)\bigr),$$

with $\omega_0=30$ and variance-preserving initialization.

• Fusion: LF and HF streams are concatenated in feature space and passed through an MLP predicting 3D keypoint sets $\hat{K} = \{\hat{k}_i\}_{i=1}^M$.

Geometry-aware Loss

• Position accuracy:

$$\mathcal{L}_{\text{pos}} = \sum_{i=1}^M \|\hat{k}_i - k_i\|_2^2$$

• Geometric/structural consistency (pairwise, modulated by SIREN periodicity):

$$\mathcal{L}_{\text{geo}} = \sum_{(i,j)\in E} \left\| \sin[\omega_0 (\hat{k}_i - \hat{k}_j)] - \sin[\omega_0 (k_i - k_j)] \right\|_2^2$$

• Temporal smoothness (optional):

$$\mathcal{L}_{\text{temp}} = \sum_{t=2}^{T}\sum_{i=1}^M \| (\hat{k}_i^t - \hat{k}_i^{t-1}) - (k_i^t - k_i^{t-1}) \|_2^2$$

• Full SirenPose loss:

$$\mathcal{L}_{\mathrm{SirenPose}} = \mathcal{L}_{\text{pos}} + \lambda_{\text{geo}}\mathcal{L}_{\text{geo}} + \lambda_{\text{temp}}\mathcal{L}_{\text{temp}}$$

• End-to-end training objective:

$$\mathcal{L}_{\text{total}} = \mathcal{L}_{\text{recon}} + \lambda_{\mathrm{sp}}\mathcal{L}_{\mathrm{SirenPose}}$$

where $\mathcal{L}_{\text{recon}}$ is a standard photometric or radiance loss.

GNN Structural Priors and High-Frequency Modeling

Adjacent keypoint pairs are encoded as edges $E$ in a graph $G=(V,E)$, with message passing layers enforcing structural correlation—critical for robust recovery under occlusion. The SIREN-based HF stream ensures that rapid, non-smooth deformations (such as high-frequency geometry or fast motion) are effectively modeled with minimal spectral bias.

6. Empirical Outcomes, Extensions, and Implications

Quantitative Results

• Benchmarks: DAVIS, Sintel, Bonn.
• Metrics (on DAVIS, SirenPose vs MoSCA):
  • FVD↓: 1197 → 984 (–17.8%)
  • FID↓: 186.4 → 132.8 (–28.7%)
  • LPIPS↓: 0.4074 → 0.3829 (–6.0%)
  • Temporal Consistency↑: 0.81 → 0.91
  • Geometric Accuracy↑: 0.75 → 0.87
• Pose Estimation (ATE, RPE-Trans, RPE-Rot) on Sintel/Bonn/DAVIS: consistently lower errors (e.g., Sintel: reduction of –13.4%, –34.8%, –36.9% versus Monst3R).
• Qualitative: Error curves smooth, no spikes; user studies rate reconstructions as more temporally coherent and visually faithful (Cai et al., 23 Dec 2025).

Dataset and GNN Pre-training

Extension of the UniKPT set from 400k to 600k annotated frames, spanning broad categories (humans, animals, vehicles, tools), enables robust GNN pre-training and keypoint supervision across diverse objects and scenes.

Robustness

• Fast motion/occlusion: HF stream and GNN prevent spatial or temporal drift, and borrow geometric context for missing keypoints.
• Multi-object scenes: Multiplexed keypoint graphs allow handling of co-located actors without cross-contamination.
• Plug-in loss: $\mathcal{L}_{\mathrm{SirenPose}}$ is compatible with diverse NeRF and 4D reconstruction backbones, typically improving FVD/FID/LPIPS by 8–20%.

7. Applications and Extensions

SirenPose System	Target Domain	Core Modality
(Marchegiani et al., 2018)	Audio event detection/localization	Stereo urban audio
(Cai et al., 23 Dec 2025)	3D dynamic scene reconstruction	Monocular video, keypoints

• Automotive and ADAS (Acoustic SirenPose): Real-time vehicle-mounted warning systems, integration with camera (pose fusion), edge or embedded deployment, extended to multi-source signal separation and 360° coverage.
• Dynamic Scene Capture (Geometric SirenPose): Physically plausible video-to-3D modeling, improved human/animal motion estimation, resilience to occlusion and fast motion, pluggable loss for 4D scene synthesis pipelines.
• A plausible implication is that future cross-modal pose estimation systems (e.g., joint audio-visual systems for robotics) could benefit from integrating both classes of SirenPose methodology.

• "Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes" (Marchegiani et al., 2018)
• "SirenPose: Dynamic Scene Reconstruction via Geometric Supervision" (Cai et al., 23 Dec 2025)