2026 · completed
Filtering noise and answering machines
Two small audio classifiers around the transcription pipeline: one filters out noise, the other recognises answering machines on outbound calls and updates the CRM.
ML research internship, AI Lab, Selectra, Madrid, June–September 2026.
- precision of the noise detector
- 98.4%
- answering machines correctly identified on a hard test set of 900 calls
- 95–97%
- outbound calls checked per day
- 7,800
How it works
Noise: two statistical filters settle the obvious cases, and only the doubtful ones reach a heavier model, so precision stays high at low cost. Answering machines: a small model listens to the first sixty seconds of each outbound call and writes the answer to the CRM about two minutes later; recordings are deleted once the result is validated.
Measured on the company’s own calls. I asked that the answering-machine flag never be used to compute agents’ bonuses.
Technical details
Noise filter
The transcription model sometimes writes words where there is only noise. The detector keeps this cheap: two statistical filters settle the obvious cases at almost no cost, and only the doubtful ones go to a heavier model. A large model on every segment would have cost far more for little gain.
- Design
- two statistical filters, then a heavier model on doubtful cases
- Result
- 98.4% precision
Answering machines
An independent project: on outbound calls, find out automatically who answered, a person or a voicemail. A small model examines only the first sixty seconds of the call, and the result is written to the CRM about two minutes after the call. Recordings are deleted once the result is validated, and I asked that the flag never be used to compute agents’ bonuses.
- Input
- first 60 s of each outbound call
- Volume
- ≈ 7,800 calls a day
- Result
- 95–97% correct on a hard test set of 900 calls
- Output
- CRM updated ≈ 2 min after the call, audio deleted