Energy-aware noise reduction for wireless acoustic sensor networks

Jie Zhang

Research output: ThesisDissertation (TU Delft)

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In speech processing applications, e.g., speech recognition, hearing aids (HAs), video conferencing, and human-computer interaction, speech enhancement or noise reduction is an essential front-end task, as the recorded speech signals are inevitably corrupted by interference, including coherent/incoherent noise and reverberation. Traditional noise reduction algorithms are mostly based on spatial filtering techniques using a microphone array. The performance of the noise reduction algorithms scales with the number of microphones that are involved in filtering, but a large-sized microphone array cannot be mounted in many realistic systems, e.g., HAs. In the last few decades, with a great development in micro-electro-mechanical systems, wireless devices are more and more commonly-used in our daily life, like the smartphone, laptop, wireless HA, and ipad. These devices have acoustic sensors equipped and a capability of wireless communication, leading to a wireless acoustic sensor network (WASN). The WASN can be organized in a centralized fashion where all the devices are only allowed to connect with a fusion center (FC), or in a decentralized way where the devices are connected with the close-by counterparts via wireless links. ThisWASN can resolve the disadvantages of the traditional microphone array systems, since thewireless devices can be placed anywhere in the vicinity and one device is able to make use of measurements from other external devices. More importantly, the acoustic scene can be sampled more comprehensively, resulting in a potential improvement in noise reduction performance.
Original languageEnglish
Awarding Institution
  • Delft University of Technology
  • Heusdens, R., Supervisor
  • Hendriks, R.C., Supervisor
Award date15 Jan 2020
Print ISBNs978-94-6366-239-0
Publication statusPublished - 2020


  • Microphone subset selection
  • rate distribution
  • noise reduction
  • binaural cue preservation
  • distributed algorithms
  • relative acoustic transfer function
  • quantization
  • bit-rate
  • power consumption
  • energy efficiency
  • wireless acoustic sensor networks

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