Audio Technology Fundamentals: From Physics to Device Implementation

The article offers a thorough overview of audio technology fundamentals, tracing the evolution of audio effects and major brands, explaining sound physics and acoustic phenomena, detailing speaker and microphone types—including MEMS and multi‑mic systems—and covering power amplifiers, codec chips, and both subjective and objective methods for evaluating audio quality.

MEMS devicesacoustic effectsaudio quality

0 likes · 53 min read

Audio Technology Fundamentals: From Physics to Device Implementation

OPPO Kernel Craftsman

Jul 21, 2023 · Frontend Development

Audio Architecture and Quality Optimization in WebRTC: Devices, 3A Processing, Codec, NetEQ and Scenario‑Based Solutions

The article explains WebRTC’s audio pipeline—from device capture through hardware or software 3A (AEC, ANS, AGC), Opus codec selection, and NetEQ jitter‑buffer handling—detailing how device specifics and scenario‑based configurations (live streaming, meetings, education, watch‑together) affect quality and why pure‑software 3A is emerging as the preferred future solution.

3AAudio ProcessingNetEQ

0 likes · 29 min read

Audio Architecture and Quality Optimization in WebRTC: Devices, 3A Processing, Codec, NetEQ and Scenario‑Based Solutions

360 Quality & Efficiency

Apr 22, 2022 · Artificial Intelligence

Audio Quality Assessment Using a BiLSTM Deep Learning Model

This article presents a no‑reference audio quality assessment system that leverages a bidirectional LSTM network to extract spectral features via FFT and predict perceptual scores, describing the architecture, technical advantages, data preparation, loss design, and TensorFlow implementation.

BiLSTMDeep LearningSignal Processing

0 likes · 8 min read

Audio Quality Assessment Using a BiLSTM Deep Learning Model

360 Quality & Efficiency

Aug 27, 2021 · Artificial Intelligence

Training an Audio Quality Detection Model Using Synthetic Noise and PESQ Scoring

This article explains how to generate low‑quality audio samples from clean speech by randomly inserting noise at various SNR levels, compute objective PESQ scores as ground‑truth, and use these paired data to train a neural‑network model for reference‑free audio quality assessment.

Neural NetworkPESQPython

0 likes · 7 min read

Training an Audio Quality Detection Model Using Synthetic Noise and PESQ Scoring

Tencent Tech

Jul 1, 2020 · Artificial Intelligence

How Context‑Based PLC Boosts VoIP Quality in Weak Networks

This article explains why VoIP calls suffer from brief interruptions caused by packet loss, compares traditional forward error correction (FEC) and packet loss concealment (PLC) techniques, introduces Tencent's deep‑learning‑enhanced cPLC, and shows how it significantly improves MOS scores, especially under burst‑loss conditions.

Deep LearningFECPLC

0 likes · 6 min read

How Context‑Based PLC Boosts VoIP Quality in Weak Networks