Leveraging AI for Robust Acoustic Echo Cancellation in Smart Communication Systems

Le résumé fourni par la source

Acoustic echo is one major problem in voice communication systems, compromising speech quality and user experience. Conventional echo cancellation methods are challenged by dynamic acoustic settings and changing noise conditions. In this paper, an AI-based acoustic echo cancellation (AEC) framework is proposed that exploits deep learning and adaptive filtering to efficiently cancel echo while maintaining speech quality. The suggested system leverages recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer-based structures to predict and model echo paths in real-time. By adopting both supervised and unsupervised learning approaches, the system remains self-sustaining in adjusting to varying acoustic conditions, hence robust across varied communication platforms, such as VoIP, video conferencing, and smart assistants. Performance assessments indicate enhanced echo suppression, reduced latency, and better speech clarity in comparison with traditional techniques. The findings identify AI's capability in transforming AEC systems for better real-time voice communication.

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