Design a Static Audio Detection System

Q: Design a Static Audio Detection System

This question evaluates system design and ML integration skills for building a scalable, reliable offline audio detection pipeline, including ingestion, preprocessing, STT and spectral feature extraction, rule-based post-processing, artifact persistence, and human-in-the-loop review within the ML system design domain.

Q: How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

Q: What difficulty level is this interview question?

This is a hard difficulty ML System Design question, commonly asked during Onsite rounds at Roblox.

Q: What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Roblox during technical interviews.

Question

System Design: Static Audio Detection Pipeline

Context

Design an offline (non-live) audio detection system that processes static audio files (e.g., user-uploaded clips) for policy compliance and quality. The goal is to ingest files, extract signals (speech-to-text, spectral features, keywords), combine them via rules, classify outcomes, and support human review where needed.

Requirements

Functional
- Ingest audio files from object storage.
- Preprocess (validation, transcoding, noise reduction, segmentation).
- Extract features: spectral analysis, speech-to-text (STT), keyword/phrase detection.
- Combine signals using a rule-based post-processor to classify each asset as: Clean, Problematic, or Needs Human Review.
- Persist artifacts (features, transcript, decisions) and expose results via API/stream.
- Discover new files automatically; support both event-driven and scheduled/batch discovery.
- Provide a manual review workflow (assignment, labeling, consensus, requeueing, audit).
- Support reprocessing/backfill when rules change.
Non-Functional
- Scalability: handle large daily volumes with predictable throughput.
- Latency: near-real-time (minutes) for most files.
- Reliability/fault tolerance: at-least-once processing, idempotent tasks, DLQs.
- Cost efficiency: optimize storage/compute and third-party API usage.
- Security/privacy: encryption at rest/in-transit, access controls, audit trails.
- Observability: metrics, logs, traces; quality and health monitoring.
Out of Scope
- Selecting or training ML models. Assume pluggable components.

Deliverables

Functional and non-functional requirements.
Key entities and data model.
High-level architecture (storage, compute, orchestration).
End-to-end processing flow from ingestion to output.
Integration of STT, spectral analysis, keyword detection, noise reduction, and rule-based post-processing.
File discovery strategy (event-driven vs cron/batch).
Outcome classification scheme and manual review workflow.
Scalability, throughput/latency targets, data retention, fault tolerance, backfill, and cost controls.
Success metrics and monitoring/alerting for quality and system health.

Design a Static Audio Detection System

Quick Overview

System Design: Static Audio Detection Pipeline

Context

Requirements

Deliverables

Solution

Comments (0)