I started with a pretty basic spectral subtraction approach and the interviewer just kind of waited, so I figured they wanted more.
Start by clarifying the requirements: what kind of noise, real-time vs offline, and quality metrics. Then propose a hybrid system combining classical signal processing (e.g., spectral subtraction) with deep learning models (e.g., U-Net on spectrograms), and discuss trade-offs in latency, accuracy, and computational cost.
Pro tip: Emphasize that noise removal is ill-posed without assumptions; explicitly state your assumptions about noise characteristics and signal type, and mention that you would evaluate with both objective metrics (e.g., SNR, PESQ) and subjective listening tests.
Ask questions to understand the use case: real-time or offline, type of noise (stationary vs non-stationary), acceptable latency, available compute, and target quality metrics.
Decide between classical methods (spectral gating, Wiener filter) and deep learning (CNN, RNN, Transformer on spectrograms). Consider a hybrid approach for robustness.
Plan how to obtain paired clean/noisy data (synthetic mixing or real recordings), augment data, and train with appropriate loss functions (e.g., L1 on spectrogram, SI-SNR).
Discuss model compression (quantization, pruning), serving infrastructure (batch vs streaming), and integration with existing audio pipelines.
Define evaluation metrics (SNR, PESQ, STOI), set up A/B testing, and plan for continuous improvement with user feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.