In a world where videoconferencing, team gaming, and voice-operated systems proliferate, it is vital to extract clear, intelligible voice from environmental noise. Ceva-ClearVox ENC software features a neural-network-based ENC algorithm with small memory and processing requirements to embed ENC into even tiny systems. The algorithm processes both outgoing and incoming speech to provide clear communications to both parties on a call
The Solution
Ceva-ClearVox ENC implements a trained neural network in embedded software, eliminating the need for external cloud connectivity. Despite the neural-network approach, ClearVox ENC requires so little memory and processing power that it can be coresident with other applications even in tiny hardware configurations. Yet the algorithm is effective enough to give excellent speech quality for videoconferencing and gaming.
Working with a single microphone input for outgoing speech and—uniquely—also processing incoming signals from the other parties in a connection, ClearVox ENC effectively separates speech from both continuous and transient background noise. Versions are available for Ceva-BX1, BX2, and SensPro2 DSP cores and for ARM MCUs.
