Audio AI Pipelines Tailored to your Needs

Deploying Machine Learning (ML) models is a multi-staged process that requires careful planning at each step. Training data acquisition or generation is a first step to successful ML deployment and has to be thoroughly planned to maximize data utility.

Augmentation techniques like equalization filtering, pitch shifting, adding reverb, or more sophisticated techniques like semi-supervised learning might later be used during model training to reduce expenses for training data collection, and therefore have to be considered already before starting data collection. Our team can plan and guide the data collection process.

Further processing with ML requires appropriate representations of the audio data. Choosing the right data representation requires domain knowledge in several fields tailored to the specific requirements of the task at hand.

Waveforms, mel spectrograms, MFCCs and various more application-specific features can be useful inputs to ML models. Besides preprocessing, postprocessing is similarly strongly application-dependent and has different needs depending on whether, e.g., real-time processing is required. We are experienced in designing the entire ML pipeline, including feature extraction, model architecture design and postprocessing.

We embrace a modern MLOps-based approach for deployment, utilising Cloud-based Infrastructure as a Service (IaaS) solutions when necessary, to deliver scalable and robust ML systems.