arXiv:2606.12662v1 Announce Type: cross Abstract: Speech enhancement models typically apply uniform capacity across all frequencies, disregarding the non-uniform spectral resolution of human hearing. We propose BASENet, a frequency-adapted architecture that partitions the spectrum into Bark-scale bands and assigns each a scaled-capacity encoder derived from critical-band density, automatically granting deeper branches to perceptually dense low frequencies and lighter ones to high frequencies. A cross-band attention module captures harmonic dependencies across bands through compact frequency-po
The continuous drive for more efficient and perceptually aligned AI models, especially in speech processing, is pushing for innovations like BASENet.
Improving speech enhancement for human hearing via frequency-adapted architectures could significantly boost natural language processing, human-computer interaction, and accessibility applications.
This research introduces a more biologically inspired approach to speech enhancement, potentially leading to more effective and less computationally intensive audio AI models.
- · AI model developers
- · Speech technology companies
- · Hearing aid manufacturers
- · Telecommunications
- · Generic uniform-capacity speech enhancement models
More accurate and natural-sounding speech enhancement in noisy environments.
Accelerated development of conversational AI and real-time audio analysis applications due to improved input quality.
Enhanced accessibility for individuals with hearing impairments through highly customized and adaptable audio processing.
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Read at arXiv cs.AI