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concept#Architecture#Software Engineering#Analytics

Audio Processing

Conceptual overview of techniques for analysing and processing audio signals used in media, communications and measurement systems.

Audio processing covers techniques for capturing, analyzing, and transforming audio signals, including filtering, compression, and feature extraction.
Established
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Intermediate

Technical context

Digital audio workstations (DAWs)Streaming and communication platformsAnalysis frameworks and libraries (librosa, SoX)

Principles & goals

Assess latency versus qualityExplicit handling of sampling rates and formatsIsolate testability of signal paths
Build
Domain, Team

Use cases & scenarios

Compromises

  • Delays from unsuitable buffering strategies
  • Loss of quality characteristics from aggressive compression
  • Legal issues when using licensed content
  • Standardized sampling rates and clear format conversion
  • Modular pipelines with clear interfaces
  • Automated tests for signal paths and quality metrics

I/O & resources

  • Raw audio tracks (WAV, FLAC, MP3)
  • Metadata (timestamps, channel info)
  • Configuration parameters (sampling rate, bit depth)
  • Processed audio (real-time or file)
  • Extracted features (spectral features, MFCC)
  • Quality metrics and metadata

Description

Audio processing covers techniques for capturing, analyzing, and transforming audio signals, including filtering, compression, and feature extraction. It is used across media production, communications, and measurement systems and connects mathematical signal processing with pragmatic constraints such as latency, quality, and resource management. Applications range from real-time effects to speech and music analysis.

✔

  • Improved audio quality and user experience
  • Automated analysis and indexing of audio content
  • Scalable pipelines for batch and real-time processing

✖

  • Real-time requirements may require complex optimizations
  • Diverse formats and sampling rates complicate integration
  • Compute and memory demands for high-resolution processing

  • End-to-end latency

    Time from input signal to output in milliseconds.

  • Signal-to-Noise Ratio (SNR)

    Measure of signal quality relative to background noise.

  • CPU/GPU utilization

    Resource usage during processing, measured as a percentage.

Echo suppression in conferencing systems

Integration of adaptive filters to reduce feedback in real time.

Feature extraction for voice assistants

Extraction of MFCCs and other features to prepare ASR models.

Noise reduction in field recordings

Batch processes to remove noise and increase signal fidelity.

1

Requirements analysis: define latency, quality, formats

2

Prototyping: evaluate core algorithms with sample data

3

Integrate and scale: deploy pipeline in target environment

⚠️ Technical debt & bottlenecks

  • Non-modular signal processing pipelines hinder refactoring
  • Missing automation for format conversions
  • Outdated libraries with security or performance issues
CPU and GPU loadLatency buffers and I/OData quality and format heterogeneity
  • Using high-resolution processing in latency-critical live systems without optimization
  • Deploying untested ML models directly into production audio pipelines
  • Neglecting metadata and timestamps for synchronization
  • Incorrect assumptions about network latency in distributed setups
  • Insufficient monitoring metrics for quality and latency
  • Considering legal restrictions on recordings too late
Digital signal processing (DSP)Audio formats and encodingProgramming (Python, C++, real-time systems)
Latency requirementsAudio quality and fidelityScalability for batch and streaming processing
  • • Available compute resources
  • • Real-time capable network infrastructure
  • • Licensing and data protection requirements