With the rapid development of communication and artificial intelligence technologies, the demand for high-quality voice communication and interaction has grown exponentially. However, noise interference and the limitations of current sound capture devices significantly reduce the precision and range of voice capture, which in turn hinders communication quality and further development of intelligent audio processing.
In noisy environments, the experience of voice communication deteriorates sharply, as noise substantially diminishes the efficiency of capturing target speech, leading to a significant decrease in intelligibility. Research indicates that when the Signal-to-Noise Ratio (SNR) drops below 0dB, it becomes challenging for the human ear to recognize speech, and when the SNR is below -10dB, extracting meaningful information becomes nearly impossible. Current mainstream voice recognition systems achieve an accuracy rate higher than 95% when the SNR exceeds 20dB, but this rate falls to below 70% at around 10dB, and to 30% at 0dB, making recognition unfeasible. Therefore, suppressing noise to enhance the SNR while minimizing speech distortion remains the ultimate goal of front-end audio processing—a highly complex global challenge.
Limitations of Traditional ENC Techniques
Most traditional ENC methods concentrate on single-microphone technologies, such as spectral subtraction, Wiener filtering, Kalman filtering, statistical modeling, and subspace techniques. These techniques typically rely on mathematical model assumptions about the properties of voice and noise signals, employing the optimization of an objective function to eliminate the estimated noise component from the mixed signal. Some common assumptions involve the premise that noise tends to be more stationary than speech, with the noise spectrum being Gaussian-distributed and the speech spectrum Gamma-distributed. Nonetheless, in practical scenarios, especially in complex environments, these assumptions often fail to hold, making it difficult for these methods to deliver effective noise reduction.
In comparison, multi-microphone processing techniques capitalize on the phase and amplitude disparities among microphones to create spatial beams aimed at the target sound source, effectively suppressing noise from other directions, thereby achieving superior noise reduction performance. Nonetheless, due to the non-idealities of these beams, the processed speech may still retain a significant amount of residual noise, potentially degrading the overall audio quality.
Global Advances in ENC Technology
Recently, Computational Auditory Scene Analysis (CASA) has been increasingly applied to ENC tasks. By analyzing the time-frequency features of speech, this technique reframes the noise reduction problem as estimating noise masking values across different time-frequency units, subsequently integrating with classification or regression tasks in machine learning. With the rise of deep learning, technologies such as Deep Neural Networks (DNNs) have been incorporated into noise reduction algorithms to learn the relationship between noisy speech features and ideal masking values. While these deep learning-based noise reduction algorithms can achieve notable results, they are often characterized by high computational complexity and are not immune to the "black box" problem,posing challenges in practical applications.
Oleap's Innovative ENC Solution
In a departure from the conventional approach, Oleap has pioneered an innovative solution that integrates microphone arrays, auditory scene analysis, deep learning, and Gammatone filter banks into a comprehensive front-end intelligent audio processing solution. This solution not only consolidates a multitude of functions—including noise suppression, reverberation elimination, array gain, and the separation, tracking, and enhancement of target voice signals—but also ensures that the target voice remains minimally distorted during the noise reduction process.
By adopting a joint modeling approach that synergizes signal processing with deep learning technologies, this solution significantly reduces computational load while mitigating the "black box" effects typically associated with deep learning systems.In diverse and complex high-noise environments, this solution delivers remarkably clear voice capture, with noise reduction levels exceeding 40dB. Comparative performance evaluations against domestic and international counterparts reveal that this innovative solution enhances noise reduction by 15-35dB and improves the quality of voice signals, as indicated by an average increase of 0.3 points in MOS across various noise conditions.
Setting New Standards in ENC
Our technological solution sets a new standard in the noise reduction domain, distinguished by three significant advantages:
- Unrivaled Noise Reduction Coupled with Minimal Distortion: In environments characterized by all kinds of noises, our technology excels in delivering exceptional noise reduction while preserving the integrity and clarity of the voice signal, establishing a new benchmark for performance within the industry.
- Robust Performance Across Varied Noise Scenarios: The solution consistently demonstrates stability in handling dynamic and complex noise scenarios, performing admirably across a wide spectrum of applications, from indoor environments to outdoor settings, and across various industry sectors.
- Joint Software-Hardware Optimization On Chip: Through the meticulous integration of software and hardware design, our solution is optimized for efficient implementation on chip platforms, ensuring low latency and high reliability—an essential requirement for real-time voice processing demands.
Conclusion
As technology continues to evolve, the significance of noise reduction in the realms of voice communication and processing is becoming increasingly pronounced. While traditional single-microphone and multi-microphone noise reduction technologies retain their relevance in certain specific contexts, the complexities of modern noise environments and the growing demand for higher quality necessitate innovative solutions that incorporate deep learning. These cutting-edge advancements undoubtedly represent the future trajectory of the industry. The ongoing progress in these technologies will provide more robust support across various voice processing scenarios, driving a comprehensive enhancement in the voice communication experience.
