Voice Signal Processing for Machine Learning. The Case of Speaker
  Isolation
Radan Ganchev
The widespread use of automated voice assistants along with other recent
technological developments have increased the demand for applications that
process audio signals and human voice in particular. Voice recognition tasks
are typically performed using artificial intelligence and machine learning
models. Even though end-to-end models exist, properly pre-processing the signal
can greatly reduce the complexity of the task and allow it to be solved with a
simpler ML model and fewer computational resources. However, ML engineers who
work on such tasks might not have a background in signal processing which is an
entirely different area of expertise.
  The objective of this work is to provide a concise comparative analysis of
Fourier and Wavelet transforms that are most commonly used as signal
decomposition methods for audio processing tasks. Metrics for evaluating speech
intelligibility are also discussed, namely Scale-Invariant Signal-to-Distortion
Ratio (SI-SDR), Perceptual Evaluation of Speech Quality (PESQ), and Short-Time
Objective Intelligibility (STOI). The level of detail in the exposition is
meant to be sufficient for an ML engineer to make informed decisions when
choosing, fine-tuning, and evaluating a decomposition method for a specific ML
model. The exposition contains mathematical definitions of the relevant
concepts accompanied with intuitive non-mathematical explanations in order to
make the text more accessible to engineers without deep expertise in signal
processing. Formal mathematical definitions and proofs of theorems are
intentionally omitted in order to keep the text concise.