Imagine a meeting recording at 8 kHz, mono, with constant hum and barely intelligible voices. Or an old archive cassette with crackle and hiss. This is standard for call centers and archives. With over 5 years in audio AI and 50+ completed projects, we build AI pipelines for audio enhancement that transform such material into clean speech in just days. Our AI audio enhancement services include audio upscaling, old recording restoration, audio denoising, mp3 artifact removal, and bandwidth extension. One client brought an 8 kHz, mono recording; we deployed a pipeline based on AudioSR and Resemble Enhance in three days. Results: PESQ from 1.8 to 3.9, STOI from 0.65 to 0.92. Voices became clear, high frequencies restored. Our client reduced subtitling costs by 50%. Contact us for a pilot project (starting at $2,000) – we'll process one of your recordings and show the result. Typical project cost ranges from $5,000 to $15,000.
Why Standard Methods Fall Short
Traditional equalizers and noise suppressors (FFmpeg anlmdn) work with the existing spectrum. They cannot recover frequencies lost during compression. The G.711 telephone codec cuts everything above 3.4 kHz – no filter can restore that data. AI models, in contrast, learn to reconstruct the spectrum from broadband speech datasets. AudioSR uses a diffusion probabilistic model to generate high-frequency components from scratch, performing spectral reconstruction and audio super-resolution. For bandwidth extension, this is the only working approach. Comparison: AI methods outperform traditional filters by 2–3 times in terms of PESQ score – typical AI improvement is 1.5–2.0 compared to 0.2–0.5 for DSP. Similarly, non-stationary noise removal is significantly better with neural networks.
| Parameter | Traditional Methods | AI Methods |
|---|---|---|
| Frequency recovery | No | Yes (up to 24 kHz) |
| Non-stationary noise removal | Poor | Good |
| PESQ improvement | 0.2–0.5 | 1.5–2.0 (3–4× better) |
| Processing speed | High | Medium (with GPU) |
How We Enhance Audio: From Analysis to Deployment
Our typical pipeline:
- Analysis of source audio: spectrogram, noise level, codec, bitrate.
- Model selection: AudioSR for upscaling, Resemble Enhance for denoising and mp3 artifact removal. AudioSR leverages a diffusion probabilistic model conditioned on low-frequency spectra; it learns the mapping to full-band audio via latent space.
- Fine-tuning (optional): for specific domains (e.g., courtroom recordings), we do few-shot fine-tuning on 10–20 minutes of labeled data.
- Integration: packaging into ONNX or Triton service, adding to processing stream.
- Testing: metrics PESQ, STOI, SI-SNR, A/B test with three listeners.
Technical Pipeline Details
For upscaling we use AudioSR – a diffusion model trained on pairs of low-frequency and high-frequency spectra, performing audio super-resolution and bandwidth extension. Resemble Enhance includes a denoising module and a U-Net enhancement module. All models are wrapped in ONNX Runtime for inference with p99 latency <50 ms on GPU T4.Case Study: Restoring an Old Archive Recording (From Our Practice)
Task: a digitized cassette lecture – 16 kHz, 8-bit, mono, strong hiss and crackle. Our client wanted clean speech for subtitles.
We applied:
- AudioSR for upscaling to 48 kHz (restored frequencies up to 24 kHz).
- Resemble Enhance in denoise+enhance mode (removed hiss, improved clarity).
- FFmpeg for final loudness normalization (LUFS -16).
Results:
| Metric | Before | After |
|---|---|---|
| PESQ | 2.1 | 3.7 |
| STOI | 0.72 | 0.91 |
| SI-SNR | 8 dB | 19 dB |
The entire process took 5 days. Our client received the pipeline code and documentation for independent deployment. Hear the difference in a demo – contact us, and we'll send a sample.
What's Included in Our Work
- Audio Analysis & Report: spectrograms, noise profiles, improvement potential.
- Model Selection & Fine-tuning: custom models for your domain.
- Pipeline Development: ready-to-deploy ONNX/Triton service with API.
- Documentation: installation guide, API reference, integration examples.
- Training: 2-hour session for your team on using and maintaining the pipeline.
- Support: 1 month of technical support after deployment.
Quality Metrics We Use
Primary objective metrics:
- PESQ (ITU-T P.862) – speech quality, target >3.5. Standardized by ITU-T.
- STOI – intelligibility, target >0.85.
- MOS-LQO – subjective quality, target >4.0.
- SI-SNR – signal-to-noise ratio, target >15 dB.
We guarantee a PESQ improvement of at least 1.0 point on your recordings. Measurements are taken before and after on a control set. Storage savings up to 30% after cleanup.
Guaranteed Results
For typical projects we deliver:
- PESQ increase from 1.8–2.0 to 3.5–4.0.
- STOI improvement from 0.65–0.75 to 0.85–0.95.
- ASR (speech recognition) error reduction by 30–50% after cleanup.
- Audio compression without quality loss – space savings up to 40%.
Use Cases
- Enhancing call center recordings before STT – recognition error drops by 30–50%.
- Preparing audio datasets for TTS fine-tuning – clean material without artifacts.
- Remastering archival materials (lectures, interviews) for streaming platforms.
- Preparing clean audio for training ASR models.
Neural networks for audio (AudioSR, Resemble Enhance) solve tasks beyond classical DSP. We bring 5+ years of audio AI experience and 50+ completed projects. Our stack: PyTorch, Hugging Face, ONNX Runtime. Get a demo pipeline for your recording – contact us. Attach audio samples, and we'll evaluate the project in 2 days and deliver a turnkey proposal. Typical project cost ranges from $5,000 to $15,000.