This study aims to present a practical application for improving sound quality in wireless acoustic sensor networks (WASNs) deployed in the often noisy Iraqi environment, using distributed signal processing techniques. Through an analytical and descriptive methodology, the proposed system is based on a hierarchical WASN architecture consisting of 20 wireless microphone nodes organized in a cluster structure. Each node optimizes the local signal using Distributed Adaptive Signal Estimation (DANSE) technology, while the cluster heads use Linear Minimum Mean Square Error (LMMSE) beamforming to combine signals within the network. The results show that the average signal-to-noise ratio (SNR) increased by up to 6.3 dB between the network groups. Bandwidth consumption is reduced by approximately 30% when choosing an adaptive microphone with an empirical signal-to-noise ratio (SNR) threshold of 11 dB while maintaining a total response time of less than 30 ms, suitable for real-time industrial communications. The results also indicate that DANSE-based distributed processing combined with a hierarchical wireless acoustic sensor network (WASN) architecture provides an efficient and scalable solution for providing reliable voice communication in harsh industrial acoustic environments.