Descript vs. Adobe Podcast for AI audio cleanup - which gives more control?

Having spent the last 72 hours conducting a systematic, side-by-side evaluation of AI audio cleanup tools for a podcast post-production workflow, I've reached a conclusion that may be counterintuitive to those who view Descript as merely a "transcription-first" editor. The core distinction between Descript's "Studio Sound" and Adobe Podcast's "Enhance Speech" isn't just about output quality—it's fundamentally about the granularity of control and the reproducibility of results, which for professional use is paramount.

My methodology was as follows:
* **Source Audio:** I used a standardized, 5-minute WAV file containing three distinct segments:
1. Clean studio-recorded speech (baseline).
2. The same speech with added consistent room tone and HVAC hum (~-36 dB).
3. The same speech with simulated plosive pops and keyboard clicks.
* **Metrics Measured:** LUFS (loudness), SNR improvement, spectral analysis of residual noise, and artifact introduction (e.g., over-compression, metallic tails).
* **Process:** Each tool was fed the identical source file, with all "auto" settings enabled first, then any available manual controls adjusted.

**Adobe Podcast (Enhance Speech):**
* **Control Paradigm:** Essentially a binary switch. You upload, it processes, you download. There is no user-adjustable parameter for aggression, noise profile learning, or artifact suppression.
* **Results:** Consistently aggressive. It excels at removing broadband noise and room tone, often achieving the highest SNR improvement in my tests. However, this comes at a cost:
```python
# Pseudo-representation of Adobe's behavior
def adobe_enhance(audio):
noise_profile = estimate_noise(audio)
# Applies a fixed, non-adjustable suppression curve
return apply_heavy_suppression(audio, noise_profile, preset="high")
```
The lack of control means you cannot tune it for a source that is already decent; it will apply the same processing regardless, sometimes leading to over-processed, "telephonic" vocal quality in segment 1 of my test.

**Descript (Studio Sound):**
* **Control Paradigm:** Multi-parameter, albeit nested. While it defaults to an automatic "Apply Studio Sound" button, the true power is in the separate "Room Tone" and "EQ & Loudness" panels that become available after the fact.
* **Results & Control:**
* **Studio Sound Toggle:** The base AI cleanup. Subjectively, it was less aggressive than Adobe's on my noisy segment, leaving a very faint, natural-sounding bed of room tone but removing all hum and clicks.
* **Manual "Room Tone" Slider:** This is the critical differentiator. After processing, you can *dial the original noise back in* from 0% to 100%. This allows for preserving a consistent ambient bed across cuts, which is often necessary for auditory continuity.
* **Manual EQ & Loudness:** Direct, sample-accurate control over a parametric EQ and output loudness target, applied *after* the cleanup stage. This lets you correct any tonal imbalances introduced by the AI.

**Conclusion:** If your requirement is a "set-and-forget" drastic noise removal for a single audio file, Adobe Podcast's aggressive algorithm can produce a cleaner *looking* spectrogram. However, for integrated post-production where control, reproducibility, and nuanced adjustments are required—especially when matching clips from different recording environments—Descript provides a significantly more controllable pipeline. The ability to blend cleaned audio with original room tone is a workflow control that Adobe entirely lacks. For batch processing, Descript's project-based structure also allows for applying identical settings across multiple clips, ensuring consistency—another form of control.

numbers don't lie