Application of Speech Signal Processing in Binaural Hearing Aids

Speech signal processing technologies play a critical role in binaural hearing aid design, aiming to enhance auditory experiences for hearing-impaired users in complex environments. Core challenges include environmental noise suppression, sound source localization enhancement, and speech signal clarification.

Noise Reduction Techniques Hearing aids require target speech extraction in noisy environments, commonly using methods like adaptive filtering and spectral subtraction. These algorithms dynamically identify noise characteristics and suppress interference while preserving main frequency components of speech, ensuring users hear clear conversations. Implementation typically involves LMS (Least Mean Squares) adaptive filters that continuously update filter coefficients based on error signals between desired and actual outputs.

Sound Source Localization Optimization Binaural hearing aids capture signals through dual-microphone arrays, calculating sound source direction using Interaural Time Difference (ITD) and Interaural Level Difference (ILD). Combined with beamforming technology, this enhances speech from specific directions while attenuating noise from other directions, helping users quickly focus on sound sources. Code implementation often involves cross-correlation algorithms for ITD calculation and FFT-based spectral analysis for ILD computation.

Personalized Signal Enhancement Dynamic compensation of speech frequency bands based on individual hearing loss curves. For example, users with high-frequency hearing loss require gain boost in corresponding frequency bands while maintaining naturalness in low frequencies. Modern hearing aids can even learn user preferences and adjust parameters in real-time using recursive algorithms that adapt processing parameters through continuous feedback loops.

Future Directions Deep learning is gradually replacing traditional algorithms, enabling more precise noise separation and scene recognition through trained models. Additionally, low-latency processing and wireless binaural synchronization technologies will further improve user experience. Implementation approaches include using CNN architectures for noise classification and RNN networks for temporal pattern recognition in acoustic environments.

Research in this field not only enhances hearing aid performance but also provides technical references for other audio devices (such as headphone noise cancellation). Algorithm developments in this area often translate to generalized audio processing applications through modular code architectures.