Audio data is fundamentally time series data. Our specialized algorithms extract meaningful patterns and insights from complex audio signals.
Identify recurring patterns and acoustic signatures within audio streams
Automatically identify outliers and unexpected audio events
Isolate and remove unwanted noise components from the signal
Forecast audio quality degradation before it becomes problematic
Capture high-resolution audio signals from multiple sources
Normalize, segment, and transform raw audio data
Apply LSTM, CNN, and transformer models to extract features
Generate actionable insights and comprehensive reports
Our time series approach enables 92% accurate noise source classification and can process multi-channel audio in real-time with OpenVINO acceleration.