Binaural Deep Learning for Real-Time Speech Enhancement in Hearing Aids: Comparing Monaural and Binaural Processing in Complex Acoustic Scenarios

Core Concepts

Deep learning-based binaural speech enhancement can outperform traditional monaural and binaural processing strategies in complex acoustic scenarios with localized interfering speakers, while performing similarly in diffuse noise conditions.

Abstract

The study explores deep neural network-based speech enhancement algorithms for hearing aids, focusing on a low-latency and computationally efficient recurrent network called GCFSnet. Two versions of the GCFSnet are evaluated - a monaural version (GCFSnet(m)) and a binaural version (GCFSnet(b)) - and compared to traditional hearing aid processing strategies like adaptive differential microphones (ADM) and binaural minimum variance distortionless response (MVDR) beamforming.
The algorithms are evaluated through subjective speech intelligibility tests with hearing-impaired listeners as well as objective metrics like HASPI and MBSTOI. The experiments are conducted in two complex acoustic scenes: one with a target speaker and two localized interfering speakers (S0N±60 IFFM), and another with a target speaker and diffuse cafeteria noise (S0Ndiff Cafeteria).
The results show that in the presence of localized interferers, the binaural GCFSnet(b) significantly outperforms the other algorithms, achieving a 2.6 dB lower speech reception threshold (SRT80) compared to the best traditional method (ADM). In the diffuse noise scenario, all enhancement algorithms perform similarly, with MVDR and the GCFSnet versions achieving the best results. The objective metrics correlate well with the subjective findings, especially when considering the median performance across listeners.
The study demonstrates the potential of deep learning-based binaural speech enhancement to improve speech intelligibility for hearing-impaired listeners in complex acoustic environments, while also highlighting the importance of evaluating such algorithms with both objective and subjective measures.

Stats

The target speaker was placed at 0° azimuth, while the two interfering speakers were placed at ±60° azimuth.
The diffuse cafeteria noise was presented at 65 dB SPL, while the target speech level was varied to adjust the SNR.

Quotes

"Deep learning has the potential to enhance speech signals and increase their intelligibility for users of hearing aids."
"While in diffuse noise, all algorithms perform similarly, the binaural deep learning approach performs best in the presence of spatial interferers."

Key Insights Distilled From

Real-time multichannel deep speech enhancement in hearing aids: Comparing monaural and binaural processing in complex acoustic scenarios

by Nils L. West... at arxiv.org 05-06-2024

https://arxiv.org/pdf/2405.01967.pdf

Deeper Inquiries

How would the performance of the GCFSnet algorithms be affected by a low-bitrate binaural link between the hearing aids, as opposed to the assumed low-latency binaural communication used in this study?

In the study, the GCFSnet algorithms were assumed to have access to binaural signals without any latency, which is not practical in real-world scenarios where wireless connections between hearing aids are preferred. If a low-bitrate binaural link were to be implemented instead of the assumed low-latency binaural communication, the performance of the GCFSnet algorithms might be affected in the following ways:

Increased Latency: A low-bitrate binaural link could introduce additional latency in the transmission of signals between the hearing aids. This latency could impact the real-time processing capabilities of the GCFSnet algorithms, especially in dynamic listening environments where quick adaptation is crucial.

Loss of Signal Fidelity: The use of a low-bitrate binaural link may result in a loss of signal fidelity during transmission. This loss of fidelity could affect the accuracy of the binaural processing performed by the GCFSnet algorithms, potentially leading to a decrease in performance.

Reduced Spatial Awareness: Binaural processing relies on accurate spatial information to enhance speech intelligibility. A low-bitrate binaural link may not be able to transmit spatial cues effectively, leading to a reduction in the algorithms' ability to separate speech from noise and localize sound sources.

Adaptation Challenges: The GCFSnet algorithms may need to be adapted to account for the limitations of a low-bitrate binaural link. This adaptation process could introduce additional complexity and may require retraining the models to perform optimally under these new communication constraints.

Overall, the performance of the GCFSnet algorithms could be compromised by the use of a low-bitrate binaural link, potentially impacting their effectiveness in enhancing speech signals in hearing aids.

How would the performance of the evaluated algorithms change if the acoustic scenes included a combination of different types of interferers, rather than just localized speech or diffuse noise?

If the acoustic scenes included a combination of different types of interferers, rather than just localized speech or diffuse noise, the performance of the evaluated algorithms could be influenced in the following ways:

Increased Complexity: Dealing with a combination of different types of interferers introduces a higher level of complexity for the algorithms. They would need to adapt to varying interference patterns and prioritize different sources based on their characteristics.

Interference Management: The algorithms would need to effectively manage multiple sources of interference, each with its own spatial and spectral properties. This could require more sophisticated processing techniques to separate the target speech from the various interferers.

Adaptive Strategies: The algorithms may need to dynamically adjust their processing strategies based on the changing acoustic environment. Adaptive algorithms that can identify and suppress specific interferers while enhancing the target speech would be beneficial in such scenarios.

Performance Trade-offs: Handling a combination of different interferers could lead to performance trade-offs. For example, prioritizing the suppression of one type of interferer may impact the processing of another, requiring the algorithms to strike a balance between different sources of interference.

Generalization Challenges: Training the algorithms to perform well in scenarios with multiple types of interferers would require a diverse and representative training dataset. Ensuring that the models generalize effectively to unseen combinations of interferers would be crucial for their real-world applicability.

In summary, incorporating a combination of different types of interferers in the acoustic scenes would present new challenges and considerations for the evaluated algorithms, requiring them to adapt to complex and dynamic listening environments.

What strategies could be explored to make the GCFSnet more adaptable to dynamic listening environments, where the target speaker location is not fixed in front of the listener?

To make the GCFSnet more adaptable to dynamic listening environments where the target speaker location is not fixed in front of the listener, several strategies could be explored:

Dynamic Steering Mechanisms: Implementing dynamic steering mechanisms in the GCFSnet algorithms that can adjust the focus of the processing based on the changing location of the target speaker. This could involve incorporating real-time tracking of the speaker's position using external devices or sensors.

Adaptive Beamforming: Utilizing adaptive beamforming techniques that can automatically adjust the beamforming direction based on the location of the target speaker. Adaptive algorithms can continuously optimize the spatial filtering to enhance the target speech and suppress interfering sources.

Environmental Awareness: Incorporating environmental awareness into the algorithms to adapt to different acoustic conditions and interference scenarios. This could involve analyzing the acoustic environment in real-time and dynamically adjusting the processing parameters accordingly.

Machine Learning for Adaptation: Leveraging machine learning algorithms to enable the GCFSnet to learn and adapt to dynamic listening environments. Training the models on diverse and dynamic datasets can improve their ability to generalize and perform effectively in changing scenarios.

User Interaction: Providing users with the ability to interact with the GCFSnet algorithms to manually adjust settings or preferences based on the listening environment. This could involve incorporating user feedback mechanisms to fine-tune the processing in real-time.

Multi-Modal Integration: Integrating multiple modalities such as eye gaze tracking, brain activity monitoring, or contextual information to enhance the adaptability of the algorithms. Combining different sources of information can improve the algorithms' ability to respond to dynamic changes in the listening environment.

By exploring these strategies, the GCFSnet algorithms can be enhanced to effectively adapt to dynamic listening environments where the target speaker location is not fixed, improving their performance and usability in real-world scenarios.