🎧 Speech Enhancement (SE)
Speech Enhancement (SE) is the task of improving the quality and intelligibility of a speech signal that has been degraded by background noise, reverberation, or other distortions. The goal is to recover a clean speech signal from a noisy or reverberant input, enabling more robust communication and accurate downstream processing (e.g., automatic speech recognition, speaker identification).
🧠 Problem Formulation
The noisy speech signal \(y(t)\) can be modeled as:
Where:
- \(x(t)\): the clean speech signal (what we aim to recover)
- \(h(t)\): the room impulse response (RIR), representing reverberation from the environment
- \(*\): denotes convolution
- \(n(t)\): the additive noise from background sources
- \(y(t)\): the observed noisy and reverberant signal
The SE task is to estimate \(\hat{x}(t) \approx x(t)\) from \(y(t)\), effectively removing both reverberation and noise.
🗣️ Clean vs. Noisy Speech
- Clean speech is recorded in quiet, controlled environments with no background noise or echo.
- Noisy speech is corrupted by external sounds such as traffic, music, or people talking.
- Reverberation results from sound reflecting off surfaces in a room, smearing the speech signal over time.
These effects can be especially problematic in real-world environments like public spaces, offices, or home assistants.
🎯 SE Target
Given a corrupted signal \(y(t)\), the goal of SE is to produce a high-quality estimate of the original \(x(t)\). This is typically done in the time-frequency domain using spectrogram-based representations.
The figure below illustrates the difference between a clean and noisy spectrogram:
- The top spectrogram reveals the clean signal, with clearer harmonic structure and less background clutter.
- The bottom spectrogram shows noisy speech with smeared formants and high energy noise across frequencies.
🧰 soundKIT for SE
soundKIT provides a full pipeline for building and deploying deep learning-based SE models:
- TFRecord-based data generation
- Feature extraction (e.g., log-power spectrogram, Mel spectrogram)
- Training with numerical or perceptual loss functions
- Evaluation with standard metrics (PESQ, STOI, SI-SDR, DNSMOS)
- Export to embedded platforms using TFLite
By enhancing the clarity of speech signals, SE improves both human listening experiences and machine understanding in noisy or reverberant conditions.