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Tencent Music Tech Team
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Tencent Music Tech Team

Public account of Tencent Music's development team, focusing on technology sharing and communication.

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Latest from Tencent Music Tech Team

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Tencent Music Tech Team
Tencent Music Tech Team
Jun 23, 2020 · Frontend Development

Automating Front-End Development Environment with Alfred Workflows and AppleScript

The article demonstrates how to replace the half‑minute, error‑prone manual steps of launching WebStorm, iTerm, Whistle proxy, and enabling Chrome extensions with a single Alfred workflow that uses AppleScript to open apps, run commands, and toggle extensions, dramatically streamlining front‑end development setup.

AlfredAppleScriptMac
0 likes · 4 min read
Automating Front-End Development Environment with Alfred Workflows and AppleScript
Tencent Music Tech Team
Tencent Music Tech Team
Jun 12, 2020 · Frontend Development

Using Jest for Front-End Unit Testing and Coverage

The article explains how to set up Jest for front‑end unit testing, demonstrates basic test writing, async handling, hooks, snapshot and React component testing, shows coverage configuration and thresholds, compares Jest’s built‑in features to Mocha’s limitations, and offers tips on concurrency, mocking, and test‑driven development.

JavaScriptJestMocking
0 likes · 21 min read
Using Jest for Front-End Unit Testing and Coverage
Tencent Music Tech Team
Tencent Music Tech Team
May 22, 2020 · Artificial Intelligence

Beat Detection: Concepts, Algorithms, and Applications

The article explains musical beat detection fundamentals, detailing traditional onset‑strength and dynamic‑programming algorithms (as in librosa), compares time‑domain and spectral methods, showcases deep‑learning advances, and describes practical applications such as audio visualisation, rhythm games, and QQ Music’s Super‑DJ automatic remix pipeline.

Audio Analysisbeat detectiondynamic programming
0 likes · 8 min read
Beat Detection: Concepts, Algorithms, and Applications
Tencent Music Tech Team
Tencent Music Tech Team
May 8, 2020 · Mobile Development

Mobile Machine Learning Frameworks Overview and Deployment Practices in Q Music

The article reviews four mobile‑focused machine‑learning frameworks—NCNN, TensorFlow Lite, PyTorch Mobile (Caffe2) and FeatherKit—detailing their size, speed, and resource trade‑offs, and explains Q Music’s edge‑inference pipeline, optimization strategies, and the challenges of performance variability on heterogeneous mobile devices.

FeatherKitMachine LearningPyTorch Mobile
0 likes · 25 min read
Mobile Machine Learning Frameworks Overview and Deployment Practices in Q Music
Tencent Music Tech Team
Tencent Music Tech Team
Apr 30, 2020 · Mobile Development

Edge Deep Learning Inference on Mobile Devices: Challenges, Hardware Diversity, and Optimization Strategies

Edge deep learning inference on mobile devices faces hardware and software fragmentation, diverse CPUs, GPUs, DSPs, and NPUs, and limited programmability; optimization techniques such as model selection, quantization, and architecture‑specific tuning enable real‑time performance, with most inference on CPUs, GPUs offering 5–10× speedups, and co‑processor support varying across Android and iOS.

DSPGPU programmingNPU
0 likes · 17 min read
Edge Deep Learning Inference on Mobile Devices: Challenges, Hardware Diversity, and Optimization Strategies
Tencent Music Tech Team
Tencent Music Tech Team
May 31, 2019 · Mobile Development

Kotlin Coroutine Failure After Resource Obfuscation in Android APK

The article explains how resource obfuscation with andResGuard removes META‑INF service files needed for the Android Main dispatcher, causing coroutines to silently fail after withContext calls, and shows that preserving the META‑INF/services directory restores proper coroutine execution.

APKAndroidCoroutines
0 likes · 8 min read
Kotlin Coroutine Failure After Resource Obfuscation in Android APK
Tencent Music Tech Team
Tencent Music Tech Team
Apr 19, 2019 · Mobile Development

Common Wrapper Classes for Android BufferQueue: Surface and SurfaceTexture

The article explains BufferQueue’s internal design and shows how Android developers typically use its wrapper classes—Surface as the producer and SurfaceTexture as the consumer—detailing their constructors, dequeue/queue workflows, lock/unlock mechanisms, and a complete SurfaceView example that illustrates buffer production and consumption by SurfaceFlinger.

AndroidBufferQueueC++
0 likes · 13 min read
Common Wrapper Classes for Android BufferQueue: Surface and SurfaceTexture
Tencent Music Tech Team
Tencent Music Tech Team
Apr 12, 2019 · Mobile Development

Understanding Android BufferQueue: Architecture and Internal Operations

Android’s BufferQueue, the core producer‑consumer mechanism behind the display pipeline, manages GraphicBuffer slots through explicit FREE‑DEQUEUED‑QUEUED‑ACQUIRED transitions, using shared memory and Binder to let producers (e.g., Views, MediaCodec) enqueue frames and consumers (e.g., SurfaceFlinger) acquire and release them efficiently.

AndroidBufferQueueGraphics
0 likes · 16 min read
Understanding Android BufferQueue: Architecture and Internal Operations
Tencent Music Tech Team
Tencent Music Tech Team
Mar 22, 2019 · Frontend Development

How to Build a Frontend User‑Behavior Tracing System for Debugging External Network Issues

This article analyzes the challenges of reproducing external‑network bugs, outlines common failure causes, and presents a complete design for a JavaScript SDK that records environment data, AJAX calls, errors, and user actions, stores them in IndexedDB, and visualizes the timeline for efficient troubleshooting.

IndexedDBJavaScriptUser Behavior Tracking
0 likes · 15 min read
How to Build a Frontend User‑Behavior Tracing System for Debugging External Network Issues