Case Study: Leading Technology Company achieves a studio-quality multilingual singing dataset for AI EQ & compression training with Shaip

Voice-Based Singing Audio Collection for EQ & Compression Algorithm Training Capturing Linguistic and Musical Diversity

Leading Technology Company needed linguistically and musically diverse, studio‑quality singing audio to train and evaluate automated EQ and compression algorithms. They engaged Shaip to handle data collection, studio recording, and native‑language transcription under strict technical requirements (multi‑mic takes, 48 kHz PCM mono WAV, short segmented transcriptions).

Shaip recruited 40 singers (10 per language: Chinese, Arabic, Spanish, Russian), delivered 20 hours of studio‑quality recordings with varied microphone settings, provided accurate transcriptions and detailed metadata in JSON, and completed the project in 18 weeks. The dataset from Shaip gave the Leading Technology Company a robust training set that improved the accuracy and multilingual/genre coverage of their EQ and compression models while meeting all quality and compliance requirements.