RT-Tango: Real-Time Distributed Binaural Speech Enhancement for Low-Power Hearing Aid Devices
Abstract
Real-time binaural speech enhancement is constrained by latency, computational cost, and inter-device communication, yet existing efficient solutions predominantly address single-channel settings. In this paper, we introduce RT-Tango, a real-time distributed binaural speech enhancement framework designed for streaming on resource-constrained platforms and specifically for hearing aids. RT-Tango relies on a two-stage distributed architecture combining perceptually motivated ERB feature compression, lightweight grouped recurrent mask estimation, and temporal sparsification to reduce computational cost. Stringent latency constraints are addressed by decoupling spectral resolution from algorithmic delay using an asymmetric STFT, together with causal recurrent inference and online estimation of spatial statistics. Experimental results show that RT-Tango achieves competitive speech enhancement while significantly reducing MACs operations and functioning at ultra-low latencies as low as 8 ms.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper presents a practical and well-engineered solution for real-time binaural speech enhancement on hearing aids, effectively balancing computational efficiency, latency, and performance through a combination of perceptual feature compression, grouped recurrent modeling, and asymmetric streaming STFT.
Methodology
The paper proposes RT-Tango, a distributed binaural speech enhancement framework specifically optimized for the stringent latency and computational constraints of hearing aids. The core technical contribution lies in the system-level integration of several efficiency-driven components: (1) Perceptually motivated ERB feature compression to reduce input dimensionality; (2) Grouped Recurrent Neural Networks (GRNNs) to parallelize processing and reduce the quadratic complexity of full-band recurrent layers; (3) Temporal sparsification (Fixed-Rate Skipping and learned gating) to reduce inference frequency; and (4) Asymmetric STFT configurations to decouple spectral resolution from algorithmic latency, enabling ultra-low latency (8 ms) streaming. The approach adapts the existing Tango architecture by replacing its CNN-based components with lightweight recurrent and grouped variants, focusing on hardware-aware efficiency rather than purely algorithmic novelty. The methodology is sound and directly addresses the trade-off between performance and resource consumption in embedded audio systems.
Results & evaluation
The experimental evaluation compares RT-Tango against the original Tango, a causal RNN variant (Tango-RNN), and a lightweight CNN baseline (GTCRN). Results are reported on a simulated binaural dataset using standard metrics (SI-SDR, SI-SIR, SI-SAR, STOI, PESQ). The paper demonstrates that RT-Tango achieves competitive performance with significantly lower computational cost (MACs/s) compared to GTCRN at similar frame rates, and lower cost than Tango-RNN while supporting higher frame rates. The ablation studies effectively isolate the contributions of grouping, temporal sparsification, and asymmetric STFT. However, the evaluation relies entirely on simulated data with measured room impulse responses, lacking real-world hardware deployment validation or subjective listening tests, which are critical for hearing aid applications. The comparison with GTCRN is somewhat uneven as GTCRN is a monaural/single-node model adapted to the setting, whereas RT-Tango leverages binaural spatial cues.
Reproducibility
The paper provides sufficient detail regarding the architecture (group sizes, STFT parameters, hop sizes) and training setup (optimizer, loss function, dataset generation protocol). The use of standard datasets (LibriSpeech, BinauRec) and metrics aids reproducibility. However, the specific implementation details of the learned skip gating mechanism and the exact hyperparameters for the online SCM estimation convergence are described qualitatively or with limited quantitative precision, which may hinder exact replication. The code is not publicly available (URL: none), which is a significant barrier to full reproducibility.
Limitations
The primary limitation is the reliance on simulated data, which may not capture the full complexity of real-world acoustic environments and hearing aid hardware imperfections (e.g., microphone mismatch, wind noise). The lack of subjective listening tests (MOS/MUSHRA) is a notable gap for a hearing aid paper, as objective metrics do not always correlate perfectly with user-perceived quality and comfort. Additionally, the "online" variant (RT-Tango-OS) shows a performance drop in SI-SDR/SI-SAR compared to the offline version, and the paper does not extensively discuss the impact of this degradation on user experience. The comparison with other distributed binaural methods is limited, as the field is still emerging.
Broader impact
This work has significant potential impact on the field of assistive listening devices. By demonstrating that high-quality binaural speech enhancement is feasible on low-power, resource-constrained hearing aids with ultra-low latency, it paves the way for more effective real-time noise reduction for hearing aid users. The techniques for efficient distributed processing and low-latency streaming are also relevant to other edge-Audio applications, such as smart speakers and wearable audio devices. The focus on perceptual metrics and interaural balance aligns well with the clinical requirements of hearing rehabilitation. This paper presents a practical and well-engineered solution for real-time binaural speech enhancement on hearing aids, effectively balancing computational efficiency, latency, and performance through a combination of perceptual feature compression, grouped recurrent modeling, and asymmetric streaming STFT.
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