This article introduces the Python library librosa, outlines its main audio processing features such as loading, visualization, MFCC, pitch detection, chromagram, and rhythm analysis, and provides complete code examples for each operation.
This article presents a comprehensive guide to automating audio testing in first‑person shooter games, covering common audio bugs, a step‑by‑step recording workflow, feature extraction techniques, similarity metrics, and practical toolchains such as Soundcard, fmedia, VBCable, and librosa.
This article introduces the fundamentals of automatic speech recognition (ASR) for food‑sound classification, explains key audio representations and modeling approaches, and provides a fully runnable Python implementation using librosa, TensorFlow/Keras, and classic machine‑learning tools to train and predict on the Tianchi competition dataset.
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.
